{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5e213742",
   "metadata": {},
   "source": [
    "## GDW数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99727a84",
   "metadata": {},
   "source": [
    "## 定义大模型接口"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae93998d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"code\": \"success\",\n",
      "  \"message\": \"你好！有什么我可以帮你的吗？\",\n",
      "  \"data\": null\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI(\n",
    "    base_url='http://172.16.98.219:3001/v1/',\n",
    "    api_key='sk-55rOJ7r4tOpaEaO735D05d6b73Bf4a09A15b78E9116f20D3',  \n",
    ")\n",
    "\n",
    "# 模型测试\n",
    "response = client.chat.completions.create(\n",
    "    model=\"Qwen3-32b\",\n",
    "    messages=[{\"role\": \"user\", \"content\": \"你好\"}],\n",
    "    # 开启JSON模式，确保模型输出为有效的JSON\n",
    "    response_format={\"type\": \"json_object\"}, \n",
    "    temperature=0.0\n",
    ")\n",
    "\n",
    "response_content = response.choices[0].message.content\n",
    "\n",
    "print(response_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "603656d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "PROMPT_TEMPLATE = \"\"\"\n",
    "# 角色\n",
    "\n",
    "你是一个精通水文学、地理信息科学（GIS）和数据科学的AI专家。你的核心任务是比对和链接来自不同全球大坝数据库的记录。\n",
    "\n",
    "# 上下文\n",
    "\n",
    "你收到了两条关于水坝的数据记录。\n",
    "\n",
    "* **记录1** 源自于 **GDW (Global Dam Watch)** 数据库。\n",
    "* **记录2** 源自于 **GDAT (Global Dam Tracker)** 数据库，其字段命名和结构与GDW不同。\n",
    "\n",
    "你的目标是严格判断这两条记录是否指向现实世界中**同一个**物理大坝实体。\n",
    "\n",
    "# 字段映射与含义指南\n",
    "\n",
    "在比对前，请使用以下**完整且严格**的指南来理解并映射两个数据源中的等价字段。这对于准确判断至关重要：\n",
    "\n",
    "| GDW 字段 (记录1) | GDAT 字段 (记录2) | 字段含义（中文） |\n",
    "| :--- | :--- | :--- |\n",
    "| `GDW_ID`, `Grand_id` | `Feature ID` | 唯一标识符 |\n",
    "| `Res_name` | `Reservoir Name` | 水库名称 |\n",
    "| `Dam_name` | `Dam Name` | 大坝名称 |\n",
    "| `Alt_name` | `Alt Dam Name`, `Alt Reservoir Name` | 大坝或水库的别名 |\n",
    "| `Dam_type` | (无直接对应) | 大坝类型 |\n",
    "| `Lake_ctrl` | (无直接对应) | 是否由自然湖泊改造 |\n",
    "| `River` | `River` | 河流名称 |\n",
    "| `Alt_river` | `Alt River` | 河流别名 |\n",
    "| `Main_basin` | `Main basin` | 主要流域 |\n",
    "| `Sub_basin` | `Sub basin` | 次级流域 |\n",
    "| `Country` | `Admin 0 (Country)` | 国家 |\n",
    "| `Sec_cntry` | `Secondary Admin 0 (Country)` | 第二国家（用于跨界） |\n",
    "| `Admin_unit` | `Admin 1 (State/Province)`, `Admin 2 (City/Region)` | 行政单位 |\n",
    "| `Sec_admin` | `Secondary Admin 1`, `Secondary Admin 2` | 第二行政单位 |\n",
    "| `Year_dam` | `Completion Year`, `Construction Year` | 建造/完工年份 |\n",
    "| `Pre_year` | (无直接对应) | 估算的建造年份 |\n",
    "| `Year_src` | `Source` (部分含义) | 年份数据来源 |\n",
    "| `Dam_hgt_m` | `Dam Height (m)` | 大坝高度（米） |\n",
    "| `Dam_len_m` | `Dam Length (m)` | 大坝长度（米） |\n",
    "| `Area_skm` | `Reservoir Area Consolidated (km2)` | 水库面积（平方千米） |\n",
    "| `Cap_mcm` | `Reservoir Capacity Consolidated (million m3)` | 水库容量（百万立方米） |\n",
    "| `Depth_m` | `Avg Reservoir Depth (m)` | 平均水深（米） |\n",
    "| `Dis_avg_ls` | (无直接对应) | 长期平均流量 |\n",
    "| `Dor_pc` | (无直接对应) | 调节度（DOR） |\n",
    "| `Elev_masl` | (无直接对应) | 海拔高度 |\n",
    "| `Catch_skm` | (无直接对应) | 流域面积 |\n",
    "| `Power_mw` | `Purpose - Hydroelectricity` (部分含义) | 水电容量（兆瓦） |\n",
    "| `Use_irri` | `Purpose - Irrigation` | 是否用于灌溉 |\n",
    "| `Use_elec` | `Purpose - Hydroelectricity` | 是否用于水力发电 |\n",
    "| `Use_supp` | `Purpose - Water Supply` | 是否用于水供应 |\n",
    "| `Use_fcon` | `Purpose - Flood Control` | 是否用于防洪 |\n",
    "| `Use_recr` | `Purpose - Recreation` | 是否用于娱乐 |\n",
    "| `Use_navi` | `Purpose - Navigation` | 是否用于航运 |\n",
    "| `Use_fish` | `Purpose - Fisheries` | 是否用于渔业 |\n",
    "| `Use_pcon` | `Purpose - Pollution Control` | 是否用于污染控制 |\n",
    "| `Use_live` | `Purpose - Livestock` | 是否用于牲畜用水 |\n",
    "| `Use_othr` | `Purpose - Others` | 是否用于其他目的 |\n",
    "| `Main_use` | `Main Purpose` | 主要用途 |\n",
    "| `Multi_dams` | `Multiple Dams` | 是否存在多个大坝 |\n",
    "| `Comments` | `Comment` | 备注 |\n",
    "| `Url` | `Source` | 相关网站URL或来源 |\n",
    "| `Quality` | `Data Quality Flag` | 数据质量指数/标记 |\n",
    "| `Editor` | `Editor` | 数据编辑者 |\n",
    "| `Long_riv`, `Long_dam`| `Decimal degree longitude` | 经度 |\n",
    "| `Lat_riv`, `Lat_dam` | `Decimal degree latitude` | 纬度 |\n",
    "| `Orig_src` | `Source` | 原始数据来源 |\n",
    "\n",
    "# “一致性”的定义 (请严格遵循)\n",
    "\n",
    "1.  **核心标识符优先**: 检查 `GDW.GDW_ID` 或 `GDW.Grand_id` 是否与 `GDAT.\"Feature ID\"` 存在关联。这是最强的匹配证据。\n",
    "2.  **核心属性匹配**: **大坝名称 (`GDW.Dam_name` vs `GDAT.\"Dam Name\"`)、河流名称 (`GDW.River` vs `GDAT.River`) 和建造年份 (`GDW.Year_dam` vs `GDAT.\"Completion Year\"`)** 是判断的核心依据。\n",
    "3.  **地理位置**: 由于两条记录的地理坐标已经通过前置算法被认为是相近的，所以坐标 (`GDW.Lat_dam`/`Long_dam` vs `GDAT.\"Decimal degree latitude\"`/`longitude`) 应作为强有力的支持证据。\n",
    "4.  **物理属性容差**: 大坝高度 (`GDW.Dam_hgt_m` vs `GDAT.\"Dam Height (m)\"`)、水库容量 (`GDW.Cap_mcm` vs `GDAT.\"Reservoir Capacity Consolidated (million m3)\"`) 等物理量，允许存在**10%以内**的合理误差。\n",
    "5.  **忽略元数据差异**: 数据来源 (`GDW.Orig_src` vs `GDAT.Source`)、编辑者 (`GDW.Editor` vs `GDAT.Editor`) 等元数据字段的差异是正常的，不应作为核心判断依据。\n",
    "6.  **处理不一致的缺失值**: `None`, `NaN`, `-99`, `-9.0` 或空字符串都应被视为空值或缺失数据。\n",
    "\n",
    "# 推理步骤 (请严格遵循)\n",
    "\n",
    "1.  **字段映射与提取**: 根据“字段映射与含义指南”，从两条记录中提取出对应的核心属性和关键物理属性。\n",
    "2.  **核心属性比对**: 严格比对提取出的核心属性。\n",
    "3.  **物理属性比对**: 在容差范围内比对关键物理属性。\n",
    "4.  **综合评估**: 综合所有比对结果，特别是核心属性的匹配情况，形成最终结论。明确说明支持你结论的关键证据。\n",
    "\n",
    "# 输出格式\n",
    "\n",
    "请务必以一个完整的JSON对象格式返回你的分析结果，不要添加任何额外的解释性文本。JSON对象应包含以下三个字段：\n",
    "\n",
    "* `is_consistent`: 布尔值 (`true` 或 `false`)，表示两条记录是否一致。\n",
    "* `confidence_score`: 浮点数 (0.0到1.0)，表示你对判断的信心程度。\n",
    "* `reasoning`: 字符串，详细说明你遵循推理步骤进行判断的完整逻辑过程。\n",
    "\n",
    "# 数据记录\n",
    "\n",
    "[记录1 - GDW]\n",
    "{record1}\n",
    "\n",
    "[记录2 - GDAT]\n",
    "{record2}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6ae5b6fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def are_records_consistent_advanced(record1: dict, record2: dict, model: str = \"Qwen3-32b\") -> dict:\n",
    "    \"\"\"\n",
    "    使用高级提示词和JSON输出来判断两条记录是否一致。\n",
    "\n",
    "    返回一个包含判断结果、信心度和理由的字典。\n",
    "    \"\"\"\n",
    "    if not client:\n",
    "        return {\"error\": \"OpenAI client is not initialized.\"}\n",
    "\n",
    "    record1_str = json.dumps(record1, indent=2, ensure_ascii=False)\n",
    "    record2_str = json.dumps(record2, indent=2, ensure_ascii=False)\n",
    "    \n",
    "    # 将格式化的记录插入到高级提示词模板中\n",
    "    prompt_template = PROMPT_TEMPLATE\n",
    "    \n",
    "    prompt = prompt_template.format(record1=record1_str, record2=record2_str)\n",
    "    \n",
    "    try:\n",
    "        response = client.chat.completions.create(\n",
    "            model=model,\n",
    "            messages=[{\"role\": \"user\", \"content\": prompt}],\n",
    "            # 开启JSON模式，确保模型输出为有效的JSON\n",
    "            response_format={\"type\": \"json_object\"}, \n",
    "            temperature=0.0\n",
    "        )\n",
    "        \n",
    "        response_content = response.choices[0].message.content\n",
    "\n",
    "        print(response_content)\n",
    "        # 解析模型返回的JSON字符串\n",
    "        result = json.loads(response_content)\n",
    "        return result\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"调用API或解析JSON时出错: {e}\")\n",
    "        return {\"is_consistent\": False, \"confidence_score\": 0.0, \"reasoning\": f\"An error occurred: {e}\"}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2705311d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   GDW_ID         RES_NAME             DAM_NAME        ALT_NAME  \\\n",
      "0       1    Lake Winnipeg               Jenpeg      Split Lake   \n",
      "1       2          Ontario             Iroquois            None   \n",
      "2       3           Baikal              Irkutsk            None   \n",
      "3       4    Lake Victoria           Owen Falls            None   \n",
      "4       5  Southern Indian  Missi Falls Control  Notigi Control   \n",
      "\n",
      "           DAM_TYPE LAKE_CTRL         RIVER ALT_RIVER MAIN_BASIN  \\\n",
      "0  Lake Control Dam       Yes        Nelson      None       None   \n",
      "1  Lake Control Dam       Yes  St. Lawrence      None       None   \n",
      "2  Lake Control Dam       Yes        Angara      None       None   \n",
      "3  Lake Control Dam       Yes    White Nile      None       Nile   \n",
      "4  Lake Control Dam       Yes     Churchill       Rat       None   \n",
      "\n",
      "       SUB_BASIN  ... LONG_DAM LAT_DAM ORIG_SRC POLY_SRC GRAND_ID  HYRIV_ID  \\\n",
      "0           None  ...      0.0     0.0    GRanD   CanVec      709  70125969   \n",
      "1           None  ...      0.0     0.0    GRanD     SWBD     1485  70444883   \n",
      "2           None  ...      0.0     0.0    GRanD     SWBD     5058  30588837   \n",
      "3  Victoria Nile  ...      0.0     0.0    GRanD     SWBD     4492  10980811   \n",
      "4           None  ...      0.0     0.0    GRanD    Other      702  70037207   \n",
      "\n",
      "   INSTREAM  HYLAK_ID   HYBAS_L12                    geometry  \n",
      "0  Instream         4  7120921060  POINT (-97.86354 53.69636)  \n",
      "1  Instream         7  7121021260  POINT (-75.79425 44.48056)  \n",
      "2  Instream        11  3120638840  POINT (104.32188 52.23439)  \n",
      "3  Instream        16  1122078520    POINT (33.19379 0.43100)  \n",
      "4  Instream        37  7120896070  POINT (-98.13295 57.36163)  \n",
      "\n",
      "[5 rows x 72 columns]\n",
      "Index(['GDW_ID', 'RES_NAME', 'DAM_NAME', 'ALT_NAME', 'DAM_TYPE', 'LAKE_CTRL',\n",
      "       'RIVER', 'ALT_RIVER', 'MAIN_BASIN', 'SUB_BASIN', 'COUNTRY', 'SEC_CNTRY',\n",
      "       'ADMIN_UNIT', 'SEC_ADMIN', 'NEAR_CITY', 'ALT_CITY', 'YEAR_DAM',\n",
      "       'PRE_YEAR', 'YEAR_SRC', 'ALT_YEAR', 'REM_YEAR', 'TIMELINE', 'YEAR_TXT',\n",
      "       'DAM_HGT_M', 'ALT_HGT_M', 'DAM_LEN_M', 'ALT_LEN_M', 'AREA_SKM',\n",
      "       'AREA_POLY', 'AREA_REP', 'AREA_MAX', 'AREA_MIN', 'CAP_MCM', 'CAP_MAX',\n",
      "       'CAP_REP', 'CAP_MIN', 'DEPTH_M', 'DIS_AVG_LS', 'DOR_PC', 'ELEV_MASL',\n",
      "       'CATCH_SKM', 'CATCH_REP', 'POWER_MW', 'DATA_INFO', 'USE_IRRI',\n",
      "       'USE_ELEC', 'USE_SUPP', 'USE_FCON', 'USE_RECR', 'USE_NAVI', 'USE_FISH',\n",
      "       'USE_PCON', 'USE_LIVE', 'USE_OTHR', 'MAIN_USE', 'MULTI_DAMS',\n",
      "       'COMMENTS', 'URL', 'QUALITY', 'EDITOR', 'LONG_RIV', 'LAT_RIV',\n",
      "       'LONG_DAM', 'LAT_DAM', 'ORIG_SRC', 'POLY_SRC', 'GRAND_ID', 'HYRIV_ID',\n",
      "       'INSTREAM', 'HYLAK_ID', 'HYBAS_L12', 'geometry'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "shapefile_path = r\"Z:\\ljj\\水库水电站数据\\全球\\GDW\\GDW_v1_0_shp\\GDW_v1_0_shp\\GDW_barriers_v1_0.shp\"\n",
    "# 读取 Shapefile 到 GeoDataFrame\n",
    "gdf = gpd.read_file(shapefile_path)\n",
    "print(gdf.head())\n",
    "print(gdf.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6bc4a6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "符合条件的中国数据条数：8238\n"
     ]
    }
   ],
   "source": [
    "# 筛选中国数据\n",
    "china_data = gdf[gdf[\"COUNTRY\"] == \"China\"]\n",
    "# 输出符合条件的记录条数\n",
    "print(f\"符合条件的中国数据条数：{len(china_data)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e5016fa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8238\n",
      "8238\n",
      "8238\n",
      "[(118.726991, 33.08953), (126.686668, 43.717337), (116.837049, 40.483149), (124.966211, 40.462665), (117.441094, 40.033694)]\n",
      "(118.726991, 33.08953)\n",
      "(126.686668, 43.717337)\n",
      "(116.837049, 40.483149)\n",
      "(124.966211, 40.462665)\n",
      "(117.441094, 40.033694)\n"
     ]
    }
   ],
   "source": [
    "# 提取经纬度为列表\n",
    "latitudes = china_data[\"LAT_RIV\"].tolist()\n",
    "longitudes = china_data[\"LONG_RIV\"].tolist()\n",
    "print(len(latitudes))\n",
    "print(len(longitudes))\n",
    "# 组成经纬度元组\n",
    "coordinates = list(zip(longitudes, latitudes))\n",
    "print(len(coordinates))\n",
    "print(coordinates[:5])\n",
    "# 打印前几个经纬度元组\n",
    "for i in range(5):\n",
    "    print(f\"({coordinates[i][0]}, {coordinates[i][1]})\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2844efee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lijunjie\\AppData\\Local\\Temp\\ipykernel_16656\\3911574273.py:5: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
      "  china_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 加载中国地图，在中国地图上绘制出经纬度\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "# 加载中国地图\n",
    "china_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "china_map = china_map[china_map['name'] == 'China']\n",
    "# 绘制中国地图\n",
    "ax = china_map.plot(figsize=(10, 10), color='white', edgecolor='black')\n",
    "# 绘制经纬度点\n",
    "ax.scatter(longitudes, latitudes, color='red', s=10)\n",
    "# 隐藏坐标轴\n",
    "ax.axis('off')\n",
    "# 显示地图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55defffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Feature_ID     Dam_Name Alt_Name Reservoir Alt_Reserv         River  \\\n",
      "0           1  Beni Bahdel     None      None       None         Tafna   \n",
      "1           2  Bou Hanifia     None      None       None     El Hammam   \n",
      "2           3     Bakhadda     None      None       None          Mina   \n",
      "3           4    Boughzoul     None      None       None  Nahar Ouasel   \n",
      "4           5     Ain Zada     None      None       None    Bou Sellam   \n",
      "\n",
      "  Alt_River           Main_basin            Sub_basin Continent  ...  \\\n",
      "0      None  Mediterranean Coast  Algerian west coast    Africa  ...   \n",
      "1      None  Mediterranean Coast  Algerian west coast    Africa  ...   \n",
      "2      None  Mediterranean Coast               Chelif    Africa  ...   \n",
      "3      None  Mediterranean Coast               Chelif    Africa  ...   \n",
      "4      None           Bou Sellam  Algerian east coast    Africa  ...   \n",
      "\n",
      "         Lat      Long Flag_Qual Capac Alt_Capac           Source Comment  \\\n",
      "0  34.710833 -1.502500         1   NaN       NaN  AQUASTAT, GRAND    None   \n",
      "1  35.291667 -0.076389         1   NaN       NaN  AQUASTAT, GRAND    None   \n",
      "2  35.345556  1.036389         1   NaN       NaN  AQUASTAT, GRAND    None   \n",
      "3  35.748333  2.775833         1   NaN       NaN  AQUASTAT, GRAND    None   \n",
      "4  36.297222  5.066667         1   NaN       NaN  AQUASTAT, GRAND    None   \n",
      "\n",
      "  Editor  Main_P_Map                   geometry  \n",
      "0    Sid  Irrigation  POINT (-1.50250 34.71083)  \n",
      "1    Sid  Irrigation  POINT (-0.07639 35.29167)  \n",
      "2    Sid  Irrigation   POINT (1.03639 35.34556)  \n",
      "3    Sid  Irrigation   POINT (2.77583 35.74833)  \n",
      "4    Sid  Irrigation   POINT (5.06667 36.29722)  \n",
      "\n",
      "[5 rows x 61 columns]\n",
      "Index(['Feature_ID', 'Dam_Name', 'Alt_Name', 'Reservoir', 'Alt_Reserv',\n",
      "       'River', 'Alt_River', 'Main_basin', 'Sub_basin', 'Continent', 'Admin0',\n",
      "       'Admin1', 'Admin2', 'Admin3', 'Sec_Admin0', 'Sec_Admin1', 'Sec_Admin2',\n",
      "       'Seco_Admin', 'Year_Fin', 'Year_Const', 'Alt_Yr_Fin', 'Height',\n",
      "       'Alt_Height', 'Length', 'Alt_Length', 'Volume_Con', 'Volume_Rep',\n",
      "       'Volume_Max', 'Volume_Min', 'Area_Con', 'Area_Pol', 'Area_Rep',\n",
      "       'Area_Max', 'Area_Min', 'Avg_Depth', 'Sediment_r', 'P_Irrig', 'P_Hydro',\n",
      "       'P_WSupp', 'P_FCont', 'P_Navig', 'P_Fishr', 'P_Livst', 'P_Recrn',\n",
      "       'P_PCont', 'P_WStor', 'P_Other', 'Main_P', 'Sec_P', 'Mult_Dams',\n",
      "       'Timeline', 'Lat', 'Long', 'Flag_Qual', 'Capac', 'Alt_Capac', 'Source',\n",
      "       'Comment', 'Editor', 'Main_P_Map', 'geometry'],\n",
      "      dtype='object')\n",
      "      Feature_ID                Dam_Name Alt_Name Reservoir Alt_Reserv  \\\n",
      "6405        6406  Dalongdong (Guangdong)     None      None       None   \n",
      "6406        6407            Tiantangshan     None      None       None   \n",
      "6407        6408               Songhuaba     None      None       None   \n",
      "6408        6409               Youluokou     None      None       None   \n",
      "6409        6410                  Hongqi     None      None       None   \n",
      "\n",
      "              River               Alt_River   Main_basin Sub_basin Continent  \\\n",
      "6405  Dalongdong He                    None         None      None      Asia   \n",
      "6406     Zeng Jiang        Trib. Dong Jiang    Zhu Jiang      None      Asia   \n",
      "6407         Jinsha  Panlong Jiang; Panlona         None      None      Asia   \n",
      "6408     Zhang Shui                    None    Gan Jiang      None      Asia   \n",
      "6409    Xiang Jiang                    None  Xiang jiang      None      Asia   \n",
      "\n",
      "      ...        Lat        Long Flag_Qual Capac Alt_Capac           Source  \\\n",
      "6405  ...  22.026944  112.641944         1   NaN       NaN  AQUASTAT, GRanD   \n",
      "6406  ...  23.795278  114.171111         1   NaN       NaN  AQUASTAT, GRanD   \n",
      "6407  ...  25.141111  102.782778         1   NaN       NaN  AQUASTAT, GRanD   \n",
      "6408  ...  25.381111  114.300278         1   NaN       NaN  AQUASTAT, GRanD   \n",
      "6409  ...  26.827917  112.145278         1   NaN       NaN  AQUASTAT, GRanD   \n",
      "\n",
      "     Comment Editor     Main_P_Map                    geometry  \n",
      "6405    None  Xinyi  Flood Control  POINT (112.64194 22.02694)  \n",
      "6406    None  Xinyi  Flood Control  POINT (114.17111 23.79528)  \n",
      "6407    None  Xinyi  Flood Control  POINT (102.78278 25.14111)  \n",
      "6408    None  Xinyi  Flood Control  POINT (114.30028 25.38111)  \n",
      "6409    None  Xinyi  Flood Control  POINT (112.14528 26.82792)  \n",
      "\n",
      "[5 rows x 61 columns]\n",
      "Index(['Feature_ID', 'Dam_Name', 'Alt_Name', 'Reservoir', 'Alt_Reserv',\n",
      "       'River', 'Alt_River', 'Main_basin', 'Sub_basin', 'Continent', 'Admin0',\n",
      "       'Admin1', 'Admin2', 'Admin3', 'Sec_Admin0', 'Sec_Admin1', 'Sec_Admin2',\n",
      "       'Seco_Admin', 'Year_Fin', 'Year_Const', 'Alt_Yr_Fin', 'Height',\n",
      "       'Alt_Height', 'Length', 'Alt_Length', 'Volume_Con', 'Volume_Rep',\n",
      "       'Volume_Max', 'Volume_Min', 'Area_Con', 'Area_Pol', 'Area_Rep',\n",
      "       'Area_Max', 'Area_Min', 'Avg_Depth', 'Sediment_r', 'P_Irrig', 'P_Hydro',\n",
      "       'P_WSupp', 'P_FCont', 'P_Navig', 'P_Fishr', 'P_Livst', 'P_Recrn',\n",
      "       'P_PCont', 'P_WStor', 'P_Other', 'Main_P', 'Sec_P', 'Mult_Dams',\n",
      "       'Timeline', 'Lat', 'Long', 'Flag_Qual', 'Capac', 'Alt_Capac', 'Source',\n",
      "       'Comment', 'Editor', 'Main_P_Map', 'geometry'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 读取另一个数据\n",
    "GDAT_bar_shapefile_path = r\"Z:\\ljj\\水库水电站数据\\全球\\GDAT\\GDAT_data_v1\\GDAT_data_v1\\data\\GDAT_v1_dams.shp\"\n",
    "gdf2 = gpd.read_file(GDAT_bar_shapefile_path)\n",
    "print(gdf2.head())\n",
    "print(gdf2.columns)\n",
    "# 筛选中国数据\n",
    "china_data2 = gdf2[(gdf2['Admin0'] == \"China\") | (gdf2['Admin1'] == \"China\") | (gdf2['Admin2'] == \"China\")]\n",
    "print(china_data2.head())\n",
    "print(china_data2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "754eaeb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1168\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lijunjie\\AppData\\Local\\Temp\\ipykernel_16656\\3149283341.py:9: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
      "  china_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 同样筛选出经纬度绘图\n",
    "latitudes2 = china_data2[\"Lat\"]\n",
    "longitudes2 = china_data2[\"Long\"]\n",
    "# 组成元组\n",
    "coordinates2 = list(zip(longitudes2, latitudes2))\n",
    "print(len(coordinates2))\n",
    "# 同样在中国地图上绘制\n",
    "# 加载中国地图\n",
    "china_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "china_map = china_map[china_map['name'] == 'China']\n",
    "# 绘制中国地图\n",
    "ax = china_map.plot(figsize=(10, 10), color='white', edgecolor='black')\n",
    "# 绘制经纬度点\n",
    "ax.scatter(longitudes2, latitudes2, color='blue', s=10)\n",
    "# 隐藏坐标轴\n",
    "ax.axis('off')\n",
    "# 显示地图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b103546d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "找到的匹配坐标对数量：315\n",
      "记录1: {'GDW_ID': 286, 'RES_NAME': None, 'DAM_NAME': 'Fengman', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Songhua Jiang', 'ALT_RIVER': None, 'MAIN_BASIN': None, 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Jilin', 'SEC_ADMIN': None, 'NEAR_CITY': 'Jilin', 'ALT_CITY': None, 'YEAR_DAM': 1941, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': -99, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1941', 'DAM_HGT_M': 146, 'ALT_HGT_M': -99, 'DAM_LEN_M': -99, 'ALT_LEN_M': -99, 'AREA_SKM': 193.669, 'AREA_POLY': 193.669, 'AREA_REP': 124.0, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 9170.0, 'CAP_MAX': -99.0, 'CAP_REP': 9170.0, 'CAP_MIN': -99.0, 'DEPTH_M': 47.3, 'DIS_AVG_LS': 377814, 'DOR_PC': 77.0, 'ELEV_MASL': 243, 'CATCH_SKM': 42875, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': None, 'USE_ELEC': 'Main', 'USE_SUPP': None, 'USE_FCON': 'Sec', 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': 'Hydroelectricity', 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '2: Good', 'EDITOR': 'McGill-BL', 'LONG_RIV': 126.686668, 'LAT_RIV': 43.717337, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5838, 'HYRIV_ID': 40231024, 'INSTREAM': 'Instream', 'HYLAK_ID': 1297, 'HYBAS_L12': 4121259510}, \n",
      "记录2: {'Feature_ID': 6826, 'Dam_Name': 'Fengman', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Songhua Jiang', 'Alt_River': None, 'Main_basin': None, 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': None, 'Admin2': 'Jilin', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': None, 'Seco_Admin': None, 'Year_Fin': '1941', 'Year_Const': None, 'Alt_Yr_Fin': None, 'Height': '146', 'Alt_Height': None, 'Length': None, 'Alt_Length': None, 'Volume_Con': 9170.0, 'Volume_Rep': 9170.0, 'Volume_Max': 9170.0, 'Volume_Min': None, 'Area_Con': 193.3, 'Area_Pol': 193.3, 'Area_Rep': '124', 'Area_Max': '193.3', 'Area_Min': '124', 'Avg_Depth': 47.4, 'Sediment_r': nan, 'P_Irrig': nan, 'P_Hydro': 1.0, 'P_WSupp': None, 'P_FCont': '1', 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Hydroelectricity', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 43.717917, 'Long': 126.68625, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Hydroelectricity'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符匹配：GDW.GDW_ID (286) 与 GDAT.Feature_ID (6826) 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称完全一致：GDW.Dam_name (\\\"Fengman\\\") == GDAT.Dam_Name (\\\"Fengman\\\")\\n   - 河流名称完全一致：GDW.River (\\\"Songhua Jiang\\\") == GDAT.River (\\\"Songhua Jiang\\\")\\n   - 建造年份完全一致：GDW.Year_dam (1941) == GDAT.Year_Fin (1941)\\n3. 地理位置高度一致：\\n   - 纬度：GDW.Lat_riv (43.717337) ≈ GDAT.Lat (43.717917)（误差 < 0.001°）\\n   - 经度：GDW.Long_riv (126.686668) ≈ GDAT.Long (126.68625)（误差 < 0.001°）\\n4. 物理属性在容差范围内一致：\\n   - 大坝高度：GDW.Dam_hgt_m (146) == GDAT.Height (146)（误差 0%）\\n   - 水库容量：GDW.Cap_mcm (9170.0) == GDAT.Volume_Con (9170.0)（误差 0%）\\n   - 水库面积：GDW.Area_skm (193.669) ≈ GDAT.Area_Con (193.3)（误差 ≈ 0.18%）\\n   - 平均水深：GDW.Depth_m (47.3) ≈ GDAT.Avg_Depth (47.4)（误差 ≈ 0.21%）\\n5. 其他关键属性匹配：\\n   - 主要用途一致：GDW.Main_use (\\\"Hydroelectricity\\\") == GDAT.Main_P (\\\"Hydroelectricity\\\")\\n   - 防洪用途一致：GDW.Use_fcon (\\\"Sec\\\") 与 GDAT.P_FCont (\\\"1\\\") 表示相同含义\\n6. 仅存在可接受的元数据差异：\\n   - 数据来源不同：GDW.Orig_src (\\\"GRanD\\\") vs GDAT.Source (\\\"AQUASTAT, GRanD\\\")\\n   - 编辑者不同：GDW.Editor (\\\"McGill-BL\\\") vs GDAT.Editor (\\\"Xinyi\\\")\\n   - 部分字段存在缺失值，但不影响核心判断\\n7. 综合评估：所有核心属性完全匹配，物理属性在容差范围内一致，地理位置高度一致，元数据差异可接受，因此判断为同一实体。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符匹配：GDW.GDW_ID (286) 与 GDAT.Feature_ID (6826) 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称完全一致：GDW.Dam_name (\\\"Fengman\\\") == GDAT.Dam_Name (\\\"Fengman\\\")\\n   - 河流名称完全一致：GDW.River (\\\"Songhua Jiang\\\") == GDAT.River (\\\"Songhua Jiang\\\")\\n   - 建造年份完全一致：GDW.Year_dam (1941) == GDAT.Year_Fin (1941)\\n3. 地理位置高度一致：\\n   - 纬度：GDW.Lat_riv (43.717337) ≈ GDAT.Lat (43.717917)（误差 < 0.001°）\\n   - 经度：GDW.Long_riv (126.686668) ≈ GDAT.Long (126.68625)（误差 < 0.001°）\\n4. 物理属性在容差范围内一致：\\n   - 大坝高度：GDW.Dam_hgt_m (146) == GDAT.Height (146)（误差 0%）\\n   - 水库容量：GDW.Cap_mcm (9170.0) == GDAT.Volume_Con (9170.0)（误差 0%）\\n   - 水库面积：GDW.Area_skm (193.669) ≈ GDAT.Area_Con (193.3)（误差 ≈ 0.18%）\\n   - 平均水深：GDW.Depth_m (47.3) ≈ GDAT.Avg_Depth (47.4)（误差 ≈ 0.21%）\\n5. 其他关键属性匹配：\\n   - 主要用途一致：GDW.Main_use (\\\"Hydroelectricity\\\") == GDAT.Main_P (\\\"Hydroelectricity\\\")\\n   - 防洪用途一致：GDW.Use_fcon (\\\"Sec\\\") 与 GDAT.P_FCont (\\\"1\\\") 表示相同含义\\n6. 仅存在可接受的元数据差异：\\n   - 数据来源不同：GDW.Orig_src (\\\"GRanD\\\") vs GDAT.Source (\\\"AQUASTAT, GRanD\\\")\\n   - 编辑者不同：GDW.Editor (\\\"McGill-BL\\\") vs GDAT.Editor (\\\"Xinyi\\\")\\n   - 部分字段存在缺失值，但不影响核心判断\\n7. 综合评估：所有核心属性完全匹配，物理属性在容差范围内一致，地理位置高度一致，元数据差异可接受，因此判断为同一实体。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 313, 'RES_NAME': 'Han Shui', 'DAM_NAME': 'Danjiangkou', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Han Shui', 'ALT_RIVER': 'Han Jiang', 'MAIN_BASIN': 'Han Shui', 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Hubei', 'SEC_ADMIN': 'Henan', 'NEAR_CITY': 'Danjiangkou', 'ALT_CITY': 'Danjiang', 'YEAR_DAM': 1973, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': 2010, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1973', 'DAM_HGT_M': 112, 'ALT_HGT_M': 97, 'DAM_LEN_M': 2494, 'ALT_LEN_M': -99, 'AREA_SKM': 286.289, 'AREA_POLY': 286.289, 'AREA_REP': 367.0, 'AREA_MAX': 8620.0, 'AREA_MIN': 300.0, 'CAP_MCM': 33910.0, 'CAP_MAX': 33910.0, 'CAP_REP': 13240.0, 'CAP_MIN': 11600.0, 'DEPTH_M': 118.4, 'DIS_AVG_LS': 1002513, 'DOR_PC': 107.3, 'ELEV_MASL': 134, 'CATCH_SKM': 94604, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': None, 'USE_ELEC': 'Main', 'USE_SUPP': None, 'USE_FCON': 'Sec', 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': 'Hydroelectricity', 'MULTI_DAMS': None, 'COMMENTS': 'Not sure about capacity and alternative area; silt problems? Dam was raised to new height of 112 meters in 2010', 'URL': 'https://en.wikipedia.org/wiki/Danjiangkou_Dam', 'QUALITY': '3: Fair', 'EDITOR': 'McGill-PB', 'LONG_RIV': 111.488542, 'LAT_RIV': 32.557586, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5306, 'HYRIV_ID': 40594233, 'INSTREAM': 'Instream', 'HYLAK_ID': 1417, 'HYBAS_L12': 4120688280}, \n",
      "记录2: {'Feature_ID': 6803, 'Dam_Name': 'Danjiangkou', 'Alt_Name': None, 'Reservoir': 'Han Shui', 'Alt_Reserv': None, 'River': 'Han Shui', 'Alt_River': 'Han Jiang', 'Main_basin': 'Han Shui', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': None, 'Admin2': 'Danjiangkou', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': 'Danjiang', 'Seco_Admin': None, 'Year_Fin': '1973', 'Year_Const': None, 'Alt_Yr_Fin': None, 'Height': '97', 'Alt_Height': None, 'Length': '2494', 'Alt_Length': None, 'Volume_Con': 20890.0, 'Volume_Rep': 13240.0, 'Volume_Max': 20890.0, 'Volume_Min': '11600', 'Area_Con': 286.3, 'Area_Pol': 286.3, 'Area_Rep': '367', 'Area_Max': '8620', 'Area_Min': '286.3', 'Avg_Depth': 73.0, 'Sediment_r': nan, 'P_Irrig': nan, 'P_Hydro': 1.0, 'P_WSupp': None, 'P_FCont': '1', 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Hydroelectricity', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 32.55777778, 'Long': 111.4877778, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': 'GRanD: Not sure about capacity and alternative area; silt problems?', 'Editor': 'Xinyi', 'Main_P_Map': 'Hydroelectricity'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=313 与 GDAT.Feature_ID=6803 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称：GDW.Dam_name=\\\"Danjiangkou\\\" 与 GDAT.Dam_Name=\\\"Danjiangkou\\\" 完全一致。\\n   - 河流名称：GDW.River=\\\"Han Shui\\\" 与 GDAT.River=\\\"Han Shui\\\" 完全一致。\\n   - 建造年份：GDW.Year_dam=1973 与 GDAT.Year_Fin=1973 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.LONG_RIV=111.488542 与 GDAT.Long=111.4877778 差异为 0.000764，小于 0.001，可视为一致。\\n   - 纬度：GDW.LAT_RIV=32.557586 与 GDAT.Lat=32.55777778 差异为 0.000191，小于 0.001，可视为一致。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m=112 与 GDAT.Height=97 差异为 13.39%，超过 10% 容差，但考虑到 GDW 提供了 Alt_Height=97，与 GDAT 一致，因此可接受。\\n   - 水库容量：GDW.Cap_mcm=33910.0 与 GDAT.Volume_Con=20890.0 差异为 38.4%，但 GDAT.Volume_Rep=13240.0 与 GDW.Cap_REP=13240.0 完全一致，且 GDW 提供了 CAP_MAX=33910.0 与 GDAT.Volume_Max=20890.0 差异为 38.4%，但考虑到 GDW 注释中提到“Dam was raised to new height of 112 meters in 2010”，这可能解释了容量差异。\\n   - 水库面积：GDW.Area_skm=286.289 与 GDAT.Area_Con=286.3 完全一致。\\n5. 其他匹配：\\n   - 水库名称：GDW.RES_NAME=\\\"Han Shui\\\" 与 GDAT.Reservoir=\\\"Han Shui\\\" 完全一致。\\n   - 国家：GDW.COUNTRY=\\\"China\\\" 与 GDAT.Admin0=\\\"China\\\" 完全一致。\\n   - 主要用途：GDW.Main_use=\\\"Hydroelectricity\\\" 与 GDAT.Main_P=\\\"Hydroelectricity\\\" 完全一致。\\n   - 注释：GDW.COMMENTS 与 GDAT.Comment 都提到了“Not sure about capacity and alternative area; silt problems?”，内容一致。\\n6. 综合评估：核心属性完全一致，地理位置高度一致，物理属性在容差范围内或有合理解释，因此判断为一致。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=313 与 GDAT.Feature_ID=6803 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称：GDW.Dam_name=\\\"Danjiangkou\\\" 与 GDAT.Dam_Name=\\\"Danjiangkou\\\" 完全一致。\\n   - 河流名称：GDW.River=\\\"Han Shui\\\" 与 GDAT.River=\\\"Han Shui\\\" 完全一致。\\n   - 建造年份：GDW.Year_dam=1973 与 GDAT.Year_Fin=1973 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.LONG_RIV=111.488542 与 GDAT.Long=111.4877778 差异为 0.000764，小于 0.001，可视为一致。\\n   - 纬度：GDW.LAT_RIV=32.557586 与 GDAT.Lat=32.55777778 差异为 0.000191，小于 0.001，可视为一致。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m=112 与 GDAT.Height=97 差异为 13.39%，超过 10% 容差，但考虑到 GDW 提供了 Alt_Height=97，与 GDAT 一致，因此可接受。\\n   - 水库容量：GDW.Cap_mcm=33910.0 与 GDAT.Volume_Con=20890.0 差异为 38.4%，但 GDAT.Volume_Rep=13240.0 与 GDW.Cap_REP=13240.0 完全一致，且 GDW 提供了 CAP_MAX=33910.0 与 GDAT.Volume_Max=20890.0 差异为 38.4%，但考虑到 GDW 注释中提到“Dam was raised to new height of 112 meters in 2010”，这可能解释了容量差异。\\n   - 水库面积：GDW.Area_skm=286.289 与 GDAT.Area_Con=286.3 完全一致。\\n5. 其他匹配：\\n   - 水库名称：GDW.RES_NAME=\\\"Han Shui\\\" 与 GDAT.Reservoir=\\\"Han Shui\\\" 完全一致。\\n   - 国家：GDW.COUNTRY=\\\"China\\\" 与 GDAT.Admin0=\\\"China\\\" 完全一致。\\n   - 主要用途：GDW.Main_use=\\\"Hydroelectricity\\\" 与 GDAT.Main_P=\\\"Hydroelectricity\\\" 完全一致。\\n   - 注释：GDW.COMMENTS 与 GDAT.Comment 都提到了“Not sure about capacity and alternative area; silt problems?”，内容一致。\\n6. 综合评估：核心属性完全一致，地理位置高度一致，物理属性在容差范围内或有合理解释，因此判断为一致。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1391, 'RES_NAME': None, 'DAM_NAME': 'Shitoukoumen', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Yinma He', 'ALT_RIVER': 'Trib. Songhua Jiang', 'MAIN_BASIN': None, 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Jilin', 'SEC_ADMIN': None, 'NEAR_CITY': 'Changchun', 'ALT_CITY': None, 'YEAR_DAM': 1965, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': -99, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1965', 'DAM_HGT_M': -99, 'ALT_HGT_M': -99, 'DAM_LEN_M': -99, 'ALT_LEN_M': -99, 'AREA_SKM': 58.229, 'AREA_POLY': 58.229, 'AREA_REP': -99.0, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 1159.1, 'CAP_MAX': -99.0, 'CAP_REP': -99.0, 'CAP_MIN': -99.0, 'DEPTH_M': 19.9, 'DIS_AVG_LS': 27812, 'DOR_PC': 132.2, 'ELEV_MASL': 182, 'CATCH_SKM': 4996, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': 'Capacity from statistics - Eq 2', 'USE_IRRI': 'Sec', 'USE_ELEC': None, 'USE_SUPP': 'Sec', 'USE_FCON': 'Main', 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': 'Flood control', 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '3: Fair', 'EDITOR': 'McGill-BL', 'LONG_RIV': 125.7462, 'LAT_RIV': 43.962626, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5836, 'HYRIV_ID': 40221874, 'INSTREAM': 'Instream', 'HYLAK_ID': 14316, 'HYBAS_L12': 4121255540}, \n",
      "记录2: {'Feature_ID': 6501, 'Dam_Name': 'Shitoukoumen', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Yinma He', 'Alt_River': 'Trib. Songhua Jiang', 'Main_basin': 'Songjua Jiang', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': 'Jilin', 'Admin2': 'Changchun', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': None, 'Seco_Admin': None, 'Year_Fin': '1965', 'Year_Const': None, 'Alt_Yr_Fin': None, 'Height': None, 'Alt_Height': None, 'Length': None, 'Alt_Length': None, 'Volume_Con': 1501.9, 'Volume_Rep': 1501.9, 'Volume_Max': 1501.9, 'Volume_Min': '1368.741', 'Area_Con': 58.1, 'Area_Pol': 58.1, 'Area_Rep': None, 'Area_Max': '58.1', 'Area_Min': None, 'Avg_Depth': 25.9, 'Sediment_r': nan, 'P_Irrig': 1.0, 'P_Hydro': nan, 'P_WSupp': '1', 'P_FCont': '1', 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Flood control', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 43.96194444, 'Long': 125.7461111, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Flood Control'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1391) 与 GDAT.Feature_ID (6501) 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称：GDW.Dam_name (Shitoukoumen) 与 GDAT.Dam_Name (Shitoukoumen) 完全一致。\\n   - 河流名称：GDW.River (Yinma He) 与 GDAT.River (Yinma He) 完全一致。\\n   - 建造年份：GDW.Year_dam (1965) 与 GDAT.Year_Fin (1965) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.LONG_RIV (125.7462) 与 GDAT.Long (125.7461111) 差异极小（0.0000889）。\\n   - 纬度：GDW.LAT_RIV (43.962626) 与 GDAT.Lat (43.96194444) 差异极小（0.00068156）。\\n4. 物理属性容差：\\n   - 水库容量：GDW.Cap_mcm (1159.1) 与 GDAT.Volume_Con (1501.9) 的差异为 29.6%，超出 10% 容差范围。但考虑到 GDW 的注释 (\\\"Capacity from statistics - Eq 2\\\")，这可能是估算方法不同导致的差异。\\n   - 平均水深：GDW.DEPTH_M (19.9) 与 GDAT.Avg_Depth (25.9) 的差异为 29.6%，同样超出 10% 容差范围。但考虑到 GDW 的注释，这可能是估算方法不同导致的差异。\\n   - 水库面积：GDW.AREA_SKM (58.229) 与 GDAT.Area_Con (58.1) 的差异为 0.22%，在容差范围内。\\n5. 其他匹配项：\\n   - 国家：GDW.COUNTRY (China) 与 GDAT.Admin0 (China) 一致。\\n   - 行政单位：GDW.ADMIN_UNIT (Jilin) 与 GDAT.Admin1 (Jilin) 一致。\\n   - 城市：GDW.NEAR_CITY (Changchun) 与 GDAT.Admin2 (Changchun) 一致。\\n   - 主要用途：GDW.MAIN_USE (Flood control) 与 GDAT.Main_P (Flood control) 一致。\\n6. 元数据差异：\\n   - 数据来源：GDW.ORIG_SRC (GRanD) 与 GDAT.Source (AQUASTAT, GRanD) 有部分重叠。\\n   - 数据质量：GDW.QUALITY (3: Fair) 与 GDAT.Flag_Qual (1) 有差异，但不影响核心判断。\\n\\n综合所有比对结果，核心属性（大坝名称、河流名称、建造年份）完全一致，地理位置高度接近，其他关键属性在容差范围内或有合理解释。因此，可以高度自信地判断这两条记录指向同一个物理大坝实体。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1391) 与 GDAT.Feature_ID (6501) 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称：GDW.Dam_name (Shitoukoumen) 与 GDAT.Dam_Name (Shitoukoumen) 完全一致。\\n   - 河流名称：GDW.River (Yinma He) 与 GDAT.River (Yinma He) 完全一致。\\n   - 建造年份：GDW.Year_dam (1965) 与 GDAT.Year_Fin (1965) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.LONG_RIV (125.7462) 与 GDAT.Long (125.7461111) 差异极小（0.0000889）。\\n   - 纬度：GDW.LAT_RIV (43.962626) 与 GDAT.Lat (43.96194444) 差异极小（0.00068156）。\\n4. 物理属性容差：\\n   - 水库容量：GDW.Cap_mcm (1159.1) 与 GDAT.Volume_Con (1501.9) 的差异为 29.6%，超出 10% 容差范围。但考虑到 GDW 的注释 (\\\"Capacity from statistics - Eq 2\\\")，这可能是估算方法不同导致的差异。\\n   - 平均水深：GDW.DEPTH_M (19.9) 与 GDAT.Avg_Depth (25.9) 的差异为 29.6%，同样超出 10% 容差范围。但考虑到 GDW 的注释，这可能是估算方法不同导致的差异。\\n   - 水库面积：GDW.AREA_SKM (58.229) 与 GDAT.Area_Con (58.1) 的差异为 0.22%，在容差范围内。\\n5. 其他匹配项：\\n   - 国家：GDW.COUNTRY (China) 与 GDAT.Admin0 (China) 一致。\\n   - 行政单位：GDW.ADMIN_UNIT (Jilin) 与 GDAT.Admin1 (Jilin) 一致。\\n   - 城市：GDW.NEAR_CITY (Changchun) 与 GDAT.Admin2 (Changchun) 一致。\\n   - 主要用途：GDW.MAIN_USE (Flood control) 与 GDAT.Main_P (Flood control) 一致。\\n6. 元数据差异：\\n   - 数据来源：GDW.ORIG_SRC (GRanD) 与 GDAT.Source (AQUASTAT, GRanD) 有部分重叠。\\n   - 数据质量：GDW.QUALITY (3: Fair) 与 GDAT.Flag_Qual (1) 有差异，但不影响核心判断。\\n\\n综合所有比对结果，核心属性（大坝名称、河流名称、建造年份）完全一致，地理位置高度接近，其他关键属性在容差范围内或有合理解释。因此，可以高度自信地判断这两条记录指向同一个物理大坝实体。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1412, 'RES_NAME': None, 'DAM_NAME': 'Selihu', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Trib. Xi Liao He', 'ALT_RIVER': None, 'MAIN_BASIN': 'Liao He', 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Nei Mongol', 'SEC_ADMIN': 'Neimeng', 'NEAR_CITY': 'Naimanqi', 'ALT_CITY': None, 'YEAR_DAM': 1965, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': -99, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1965', 'DAM_HGT_M': -99, 'ALT_HGT_M': -99, 'DAM_LEN_M': -99, 'ALT_LEN_M': -99, 'AREA_SKM': 21.636, 'AREA_POLY': 21.636, 'AREA_REP': 45.0, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 120.0, 'CAP_MAX': -99.0, 'CAP_REP': 120.0, 'CAP_MIN': 100.0, 'DEPTH_M': 5.5, 'DIS_AVG_LS': 937, 'DOR_PC': 406.1, 'ELEV_MASL': 375, 'CATCH_SKM': 2202, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': 'Main', 'USE_ELEC': None, 'USE_SUPP': None, 'USE_FCON': None, 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': 'Irrigation', 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '3: Fair', 'EDITOR': 'McGill-BL', 'LONG_RIV': 120.586902, 'LAT_RIV': 42.762685, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5845, 'HYRIV_ID': 40264588, 'INSTREAM': 'Instream', 'HYLAK_ID': 14388, 'HYBAS_L12': 4121274050}, \n",
      "记录2: {'Feature_ID': 7079, 'Dam_Name': 'Selihu', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Trib. Xi Liao He', 'Alt_River': None, 'Main_basin': 'Liao He', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': 'Inner Mongolia', 'Admin2': 'Naimanqi', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': None, 'Seco_Admin': None, 'Year_Fin': '1965', 'Year_Const': None, 'Alt_Yr_Fin': None, 'Height': None, 'Alt_Height': None, 'Length': None, 'Alt_Length': None, 'Volume_Con': 120.0, 'Volume_Rep': 120.0, 'Volume_Max': 120.0, 'Volume_Min': '100', 'Area_Con': 21.6, 'Area_Pol': 21.6, 'Area_Rep': '45', 'Area_Max': '45', 'Area_Min': '21.6', 'Avg_Depth': 5.6, 'Sediment_r': nan, 'P_Irrig': 1.0, 'P_Hydro': nan, 'P_WSupp': None, 'P_FCont': None, 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Irrigation', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 42.76277778, 'Long': 120.5869444, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Irrigation'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1412) 与 GDAT.Feature_ID (7079) 不同，但这是预期的，因为它们来自不同数据库。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name (Selihu) 与 GDAT.Dam_Name (Selihu) 完全一致。\\n   - 河流名称：GDW.River (Trib. Xi Liao He) 与 GDAT.River (Trib. Xi Liao He) 完全一致。\\n   - 建造年份：GDW.Year_dam (1965) 与 GDAT.Year_Fin (1965) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.LONG_RIV (120.586902) 与 GDAT.Long (120.5869444) 差异为 0.0000424，非常接近。\\n   - 纬度：GDW.LAT_RIV (42.762685) 与 GDAT.Lat (42.76277778) 差异为 0.00009278，非常接近。\\n4. 物理属性容差：\\n   - 水库容量：GDW.Cap_mcm (120.0) 与 GDAT.Volume_Con (120.0) 完全一致。\\n   - 水库面积：GDW.AREA_SKM (21.636) 与 GDAT.Area_Con (21.6) 的差异为 0.036，小于 10% 容差。\\n   - 平均水深：GDW.DEPTH_M (5.5) 与 GDAT.Avg_Depth (5.6) 的差异为 0.1，小于 10% 容差。\\n5. 其他匹配项：\\n   - 主要用途：GDW.Main_use (Irrigation) 与 GDAT.Main_P (Irrigation) 完全一致。\\n   - 国家：GDW.COUNTRY (China) 与 GDAT.Admin0 (China) 完全一致。\\n   - 行政单位：GDW.ADMIN_UNIT (Nei Mongol) 与 GDAT.Admin1 (Inner Mongolia) 是同一地区不同翻译。\\n   - 主要流域：GDW.MAIN_BASIN (Liao He) 与 GDAT.Main_basin (Liao He) 完全一致。\\n6. 忽略元数据差异：\\n   - 数据来源和编辑者存在差异，但这是预期的，因为它们来自不同数据库。\\n7. 处理不一致的缺失值：\\n   - 一些物理属性如大坝高度和长度在两个记录中都缺失，这不影响判断。\\n综合所有比对结果，核心属性完全匹配，地理位置非常接近，物理属性在容差范围内，因此可以确定这两条记录指向同一个物理大坝实体。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1412) 与 GDAT.Feature_ID (7079) 不同，但这是预期的，因为它们来自不同数据库。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name (Selihu) 与 GDAT.Dam_Name (Selihu) 完全一致。\\n   - 河流名称：GDW.River (Trib. Xi Liao He) 与 GDAT.River (Trib. Xi Liao He) 完全一致。\\n   - 建造年份：GDW.Year_dam (1965) 与 GDAT.Year_Fin (1965) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.LONG_RIV (120.586902) 与 GDAT.Long (120.5869444) 差异为 0.0000424，非常接近。\\n   - 纬度：GDW.LAT_RIV (42.762685) 与 GDAT.Lat (42.76277778) 差异为 0.00009278，非常接近。\\n4. 物理属性容差：\\n   - 水库容量：GDW.Cap_mcm (120.0) 与 GDAT.Volume_Con (120.0) 完全一致。\\n   - 水库面积：GDW.AREA_SKM (21.636) 与 GDAT.Area_Con (21.6) 的差异为 0.036，小于 10% 容差。\\n   - 平均水深：GDW.DEPTH_M (5.5) 与 GDAT.Avg_Depth (5.6) 的差异为 0.1，小于 10% 容差。\\n5. 其他匹配项：\\n   - 主要用途：GDW.Main_use (Irrigation) 与 GDAT.Main_P (Irrigation) 完全一致。\\n   - 国家：GDW.COUNTRY (China) 与 GDAT.Admin0 (China) 完全一致。\\n   - 行政单位：GDW.ADMIN_UNIT (Nei Mongol) 与 GDAT.Admin1 (Inner Mongolia) 是同一地区不同翻译。\\n   - 主要流域：GDW.MAIN_BASIN (Liao He) 与 GDAT.Main_basin (Liao He) 完全一致。\\n6. 忽略元数据差异：\\n   - 数据来源和编辑者存在差异，但这是预期的，因为它们来自不同数据库。\\n7. 处理不一致的缺失值：\\n   - 一些物理属性如大坝高度和长度在两个记录中都缺失，这不影响判断。\\n综合所有比对结果，核心属性完全匹配，地理位置非常接近，物理属性在容差范围内，因此可以确定这两条记录指向同一个物理大坝实体。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1413, 'RES_NAME': None, 'DAM_NAME': 'Hongshan', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Laoha He', 'ALT_RIVER': 'Xiliao He', 'MAIN_BASIN': 'Liao He', 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Nei Mongol', 'SEC_ADMIN': None, 'NEAR_CITY': 'Chifeng', 'ALT_CITY': None, 'YEAR_DAM': 1964, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': 1965, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1964', 'DAM_HGT_M': 31, 'ALT_HGT_M': -99, 'DAM_LEN_M': 1174, 'ALT_LEN_M': -99, 'AREA_SKM': 59.488, 'AREA_POLY': 59.488, 'AREA_REP': 2.2, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 2600.0, 'CAP_MAX': 2600.0, 'CAP_REP': 2560.0, 'CAP_MIN': -99.0, 'DEPTH_M': 43.7, 'DIS_AVG_LS': 18735, 'DOR_PC': 440.1, 'ELEV_MASL': 433, 'CATCH_SKM': 24738, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': 'Main', 'USE_ELEC': 'Sec', 'USE_SUPP': None, 'USE_FCON': 'Sec', 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': 'Irrigation', 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '4: Poor', 'EDITOR': 'McGill-BL', 'LONG_RIV': 119.696804, 'LAT_RIV': 42.750852, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5183, 'HYRIV_ID': 40265213, 'INSTREAM': 'Instream', 'HYLAK_ID': 14389, 'HYBAS_L12': 4120354030}, \n",
      "记录2: {'Feature_ID': 7078, 'Dam_Name': 'Hongshan (Inner Mongolia)', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Laoha He', 'Alt_River': 'Xiliao He', 'Main_basin': 'Liao He', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': 'Inner Mongolia', 'Admin2': 'Chifeng', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': None, 'Seco_Admin': None, 'Year_Fin': '1964', 'Year_Const': None, 'Alt_Yr_Fin': '1965', 'Height': '31', 'Alt_Height': None, 'Length': '1174', 'Alt_Length': None, 'Volume_Con': 2560.0, 'Volume_Rep': 2560.0, 'Volume_Max': 2600.0, 'Volume_Min': None, 'Area_Con': 59.4, 'Area_Pol': 59.4, 'Area_Rep': '2.2', 'Area_Max': '59.4', 'Area_Min': '2.2', 'Avg_Depth': 43.1, 'Sediment_r': nan, 'P_Irrig': 1.0, 'P_Hydro': 1.0, 'P_WSupp': None, 'P_FCont': '1', 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Irrigation', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 42.75125, 'Long': 119.6961111, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Irrigation'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=1413 与 GDAT.Feature_ID=7078 无直接关联，但这是正常情况，因为不同数据库的ID通常不一致。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name=\\\"Hongshan\\\" 与 GDAT.Dam_Name=\\\"Hongshan (Inner Mongolia)\\\" 基本一致，仅行政区划信息不同。\\n   - 河流名称：GDW.River=\\\"Laoha He\\\" 与 GDAT.River=\\\"Laoha He\\\" 完全一致。\\n   - 建造年份：GDW.Year_dam=1964 与 GDAT.Year_Fin=1964 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv=119.696804 与 GDAT.Long=119.6961111 差异为 0.000693，非常接近。\\n   - 纬度：GDW.Lat_riv=42.750852 与 GDAT.Lat=42.75125 差异为 0.000398，非常接近。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m=31 与 GDAT.Height=31 完全一致。\\n   - 水库容量：GDW.Cap_mcm=2600.0 与 GDAT.Volume_Con=2560.0，差异为 40.0，占 1.536%，在10%容差范围内。\\n   - 水库面积：GDW.Area_skm=59.488 与 GDAT.Area_Con=59.4，差异为 0.088，占 0.148%，在10%容差范围内。\\n5. 其他关键信息：\\n   - 主要用途：GDW.Main_use=\\\"Irrigation\\\" 与 GDAT.Main_P=\\\"Irrigation\\\" 完全一致。\\n   - 行政信息：GDW.Admin_unit=\\\"Nei Mongol\\\" 与 GDAT.Admin1=\\\"Inner Mongolia\\\" 实际上是同一地区（内蒙古）的不同拼写。\\n   - 流域信息：GDW.Main_basin=\\\"Liao He\\\" 与 GDAT.Main_basin=\\\"Liao He\\\" 完全一致。\\n   - 国家信息：GDW.Country=\\\"China\\\" 与 GDAT.Admin0=\\\"China\\\" 完全一致。\\n6. 不一致但可接受的差异：\\n   - GDAT.Dam_Name 包含了 \\\"(Inner Mongolia)\\\"，这可能是为了区分同名大坝，但本质上指向同一实体。\\n   - GDW.YEAR_SRC=\\\"GRanD\\\" 与 GDAT.Source=\\\"AQUASTAT, GRanD\\\"，数据来源部分重叠。\\n   - GDW.QUALITY=\\\"4: Poor\\\" 与 GDAT.Flag_Qual=\\\"1\\\"，数据质量标记不同，但这是正常情况，不应作为核心判断依据。\\n综合所有比对结果，这两条记录指向现实世界中同一个物理大坝实体，因此判断为一致。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=1413 与 GDAT.Feature_ID=7078 无直接关联，但这是正常情况，因为不同数据库的ID通常不一致。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name=\\\"Hongshan\\\" 与 GDAT.Dam_Name=\\\"Hongshan (Inner Mongolia)\\\" 基本一致，仅行政区划信息不同。\\n   - 河流名称：GDW.River=\\\"Laoha He\\\" 与 GDAT.River=\\\"Laoha He\\\" 完全一致。\\n   - 建造年份：GDW.Year_dam=1964 与 GDAT.Year_Fin=1964 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv=119.696804 与 GDAT.Long=119.6961111 差异为 0.000693，非常接近。\\n   - 纬度：GDW.Lat_riv=42.750852 与 GDAT.Lat=42.75125 差异为 0.000398，非常接近。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m=31 与 GDAT.Height=31 完全一致。\\n   - 水库容量：GDW.Cap_mcm=2600.0 与 GDAT.Volume_Con=2560.0，差异为 40.0，占 1.536%，在10%容差范围内。\\n   - 水库面积：GDW.Area_skm=59.488 与 GDAT.Area_Con=59.4，差异为 0.088，占 0.148%，在10%容差范围内。\\n5. 其他关键信息：\\n   - 主要用途：GDW.Main_use=\\\"Irrigation\\\" 与 GDAT.Main_P=\\\"Irrigation\\\" 完全一致。\\n   - 行政信息：GDW.Admin_unit=\\\"Nei Mongol\\\" 与 GDAT.Admin1=\\\"Inner Mongolia\\\" 实际上是同一地区（内蒙古）的不同拼写。\\n   - 流域信息：GDW.Main_basin=\\\"Liao He\\\" 与 GDAT.Main_basin=\\\"Liao He\\\" 完全一致。\\n   - 国家信息：GDW.Country=\\\"China\\\" 与 GDAT.Admin0=\\\"China\\\" 完全一致。\\n6. 不一致但可接受的差异：\\n   - GDAT.Dam_Name 包含了 \\\"(Inner Mongolia)\\\"，这可能是为了区分同名大坝，但本质上指向同一实体。\\n   - GDW.YEAR_SRC=\\\"GRanD\\\" 与 GDAT.Source=\\\"AQUASTAT, GRanD\\\"，数据来源部分重叠。\\n   - GDW.QUALITY=\\\"4: Poor\\\" 与 GDAT.Flag_Qual=\\\"1\\\"，数据质量标记不同，但这是正常情况，不应作为核心判断依据。\\n综合所有比对结果，这两条记录指向现实世界中同一个物理大坝实体，因此判断为一致。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1415, 'RES_NAME': None, 'DAM_NAME': 'Nanchengzi', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Ye He', 'ALT_RIVER': 'Trib. Qou He', 'MAIN_BASIN': 'Liao He', 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Liaoning', 'SEC_ADMIN': None, 'NEAR_CITY': 'Tieling', 'ALT_CITY': 'Kaiyuan; Changtu', 'YEAR_DAM': 1962, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': 1964, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1962', 'DAM_HGT_M': 30, 'ALT_HGT_M': -99, 'DAM_LEN_M': 558, 'ALT_LEN_M': -99, 'AREA_SKM': 13.298, 'AREA_POLY': 13.298, 'AREA_REP': 14.6, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 206.0, 'CAP_MAX': 206.0, 'CAP_REP': 204.0, 'CAP_MIN': 200.0, 'DEPTH_M': 15.5, 'DIS_AVG_LS': 2636, 'DOR_PC': 247.8, 'ELEV_MASL': 148, 'CATCH_SKM': 641, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': 'Sec', 'USE_ELEC': 'Sec', 'USE_SUPP': None, 'USE_FCON': 'Main', 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': 'Flood control', 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '3: Fair', 'EDITOR': 'McGill-BL', 'LONG_RIV': 124.305208, 'LAT_RIV': 42.74043, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5846, 'HYRIV_ID': 40266197, 'INSTREAM': 'Instream', 'HYLAK_ID': 14392, 'HYBAS_L12': 4120358180}, \n",
      "记录2: {'Feature_ID': 6498, 'Dam_Name': 'Nanchengzi', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Ye He', 'Alt_River': 'Trib. Qou He', 'Main_basin': 'Liao He', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': None, 'Admin2': 'Tieling', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': 'Kaiyuan; Changtu', 'Seco_Admin': None, 'Year_Fin': '1962', 'Year_Const': None, 'Alt_Yr_Fin': '1964', 'Height': '30', 'Alt_Height': None, 'Length': '558', 'Alt_Length': None, 'Volume_Con': 206.0, 'Volume_Rep': 204.0, 'Volume_Max': 206.0, 'Volume_Min': '200', 'Area_Con': 13.3, 'Area_Pol': 13.3, 'Area_Rep': '14.6', 'Area_Max': '14.6', 'Area_Min': '13.3', 'Avg_Depth': 15.5, 'Sediment_r': nan, 'P_Irrig': 1.0, 'P_Hydro': 1.0, 'P_WSupp': None, 'P_FCont': '1', 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Flood control', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 42.74111111, 'Long': 124.3044444, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Flood Control'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=1415 与 GDAT.Feature_ID=6498 不同，但这是预期的，因为它们来自不同数据库。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name=\\\"Nanchengzi\\\" 与 GDAT.Dam_Name=\\\"Nanchengzi\\\" 完全一致。\\n   - 河流名称：GDW.River=\\\"Ye He\\\" 与 GDAT.River=\\\"Ye He\\\" 完全一致。\\n   - 建造年份：GDW.Year_dam=1962 与 GDAT.Year_Fin=1962 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv=124.305208 与 GDAT.Long=124.3044444 差异为 0.0007636，小于 0.001 的容差。\\n   - 纬度：GDW.Lat_riv=42.74043 与 GDAT.Lat=42.74111111 差异为 0.00068111，小于 0.001 的容差。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m=30 与 GDAT.Height=30 完全一致。\\n   - 水库容量：GDW.Cap_mcm=206.0 与 GDAT.Volume_Con=206.0 完全一致。\\n   - 水库面积：GDW.Area_skm=13.298 与 GDAT.Area_Con=13.3 差异为 0.002，小于 10% 的容差。\\n5. 其他匹配项：\\n   - 主要流域：GDW.Main_basin=\\\"Liao He\\\" 与 GDAT.Main_basin=\\\"Liao He\\\" 完全一致。\\n   - 国家：GDW.Country=\\\"China\\\" 与 GDAT.Admin0=\\\"China\\\" 完全一致。\\n   - 行政单位：GDW.Admin_unit=\\\"Liaoning\\\" 与 GDAT.Admin2=\\\"Tieling\\\" 一致，因为 Tieling 是 Liaoning 省的城市。\\n   - 主要用途：GDW.Main_use=\\\"Flood control\\\" 与 GDAT.Main_P=\\\"Flood control\\\" 完全一致。\\n6. 忽略元数据差异：\\n   - 数据来源：GDW.Orig_src=\\\"GRanD\\\" 与 GDAT.Source=\\\"AQUASTAT, GRanD\\\" 有部分重叠。\\n   - 编辑者：GDW.Editor=\\\"McGill-BL\\\" 与 GDAT.Editor=\\\"Xinyi\\\" 不同，但这是预期的。\\n7. 处理不一致的缺失值：\\n   - 一些字段在两个记录中都为 null 或 NaN，这在两个数据库中是正常的。\\n\\n综合所有比对结果，核心属性和关键物理属性都高度一致，地理位置非常接近，因此判断这两条记录指向同一个物理大坝实体。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=1415 与 GDAT.Feature_ID=6498 不同，但这是预期的，因为它们来自不同数据库。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name=\\\"Nanchengzi\\\" 与 GDAT.Dam_Name=\\\"Nanchengzi\\\" 完全一致。\\n   - 河流名称：GDW.River=\\\"Ye He\\\" 与 GDAT.River=\\\"Ye He\\\" 完全一致。\\n   - 建造年份：GDW.Year_dam=1962 与 GDAT.Year_Fin=1962 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv=124.305208 与 GDAT.Long=124.3044444 差异为 0.0007636，小于 0.001 的容差。\\n   - 纬度：GDW.Lat_riv=42.74043 与 GDAT.Lat=42.74111111 差异为 0.00068111，小于 0.001 的容差。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m=30 与 GDAT.Height=30 完全一致。\\n   - 水库容量：GDW.Cap_mcm=206.0 与 GDAT.Volume_Con=206.0 完全一致。\\n   - 水库面积：GDW.Area_skm=13.298 与 GDAT.Area_Con=13.3 差异为 0.002，小于 10% 的容差。\\n5. 其他匹配项：\\n   - 主要流域：GDW.Main_basin=\\\"Liao He\\\" 与 GDAT.Main_basin=\\\"Liao He\\\" 完全一致。\\n   - 国家：GDW.Country=\\\"China\\\" 与 GDAT.Admin0=\\\"China\\\" 完全一致。\\n   - 行政单位：GDW.Admin_unit=\\\"Liaoning\\\" 与 GDAT.Admin2=\\\"Tieling\\\" 一致，因为 Tieling 是 Liaoning 省的城市。\\n   - 主要用途：GDW.Main_use=\\\"Flood control\\\" 与 GDAT.Main_P=\\\"Flood control\\\" 完全一致。\\n6. 忽略元数据差异：\\n   - 数据来源：GDW.Orig_src=\\\"GRanD\\\" 与 GDAT.Source=\\\"AQUASTAT, GRanD\\\" 有部分重叠。\\n   - 编辑者：GDW.Editor=\\\"McGill-BL\\\" 与 GDAT.Editor=\\\"Xinyi\\\" 不同，但这是预期的。\\n7. 处理不一致的缺失值：\\n   - 一些字段在两个记录中都为 null 或 NaN，这在两个数据库中是正常的。\\n\\n综合所有比对结果，核心属性和关键物理属性都高度一致，地理位置非常接近，因此判断这两条记录指向同一个物理大坝实体。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1417, 'RES_NAME': None, 'DAM_NAME': 'Wolonghu', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Trib. Xiushui He', 'ALT_RIVER': None, 'MAIN_BASIN': 'Liao He', 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Liaoning', 'SEC_ADMIN': None, 'NEAR_CITY': 'Kangping', 'ALT_CITY': None, 'YEAR_DAM': 1958, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': -99, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1958', 'DAM_HGT_M': -99, 'ALT_HGT_M': -99, 'DAM_LEN_M': -99, 'ALT_LEN_M': -99, 'AREA_SKM': 58.193, 'AREA_POLY': 58.193, 'AREA_REP': 64.0, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 100.0, 'CAP_MAX': -99.0, 'CAP_REP': 100.0, 'CAP_MIN': 10.0, 'DEPTH_M': 1.7, 'DIS_AVG_LS': 2979, 'DOR_PC': 106.4, 'ELEV_MASL': 88, 'CATCH_SKM': 1316, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': None, 'USE_ELEC': None, 'USE_SUPP': None, 'USE_FCON': None, 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': None, 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '3: Fair', 'EDITOR': 'McGill-BL', 'LONG_RIV': 123.304822, 'LAT_RIV': 42.73307, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5847, 'HYRIV_ID': 40268977, 'INSTREAM': 'Instream', 'HYLAK_ID': 14394, 'HYBAS_L12': 4120360330}, \n",
      "记录2: {'Feature_ID': 7544, 'Dam_Name': 'Wolonghu', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Trib. Xiushui He', 'Alt_River': None, 'Main_basin': 'Liao He', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': None, 'Admin2': 'Kangping', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': None, 'Seco_Admin': None, 'Year_Fin': '1958', 'Year_Const': None, 'Alt_Yr_Fin': None, 'Height': None, 'Alt_Height': None, 'Length': None, 'Alt_Length': None, 'Volume_Con': 100.0, 'Volume_Rep': 100.0, 'Volume_Max': 100.0, 'Volume_Min': '10', 'Area_Con': 58.1, 'Area_Pol': 58.1, 'Area_Rep': '64', 'Area_Max': '64', 'Area_Min': '58.1', 'Avg_Depth': 1.7, 'Sediment_r': nan, 'P_Irrig': nan, 'P_Hydro': nan, 'P_WSupp': None, 'P_FCont': None, 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Unspecified purpose', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 42.73277778, 'Long': 123.3044444, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Other'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1417) 与 GDAT.Feature_ID (7544) 不同，但这是预期的，因为它们来自不同数据库。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name (\\\"Wolonghu\\\") 与 GDAT.Dam_Name (\\\"Wolonghu\\\") 完全一致。\\n   - 河流名称：GDW.River (\\\"Trib. Xiushui He\\\") 与 GDAT.River (\\\"Trib. Xiushui He\\\") 完全一致。\\n   - 建造年份：GDW.Year_dam (1958) 与 GDAT.Year_Fin (1958) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv (123.304822) 与 GDAT.Long (123.3044444) 差异为 0.0003776，非常接近。\\n   - 纬度：GDW.Lat_riv (42.73307) 与 GDAT.Lat (42.73277778) 差异为 0.00029222，非常接近。\\n4. 物理属性容差：\\n   - 水库容量：GDW.Cap_mcm (100.0) 与 GDAT.Volume_Con (100.0) 完全一致。\\n   - 水库面积：GDW.Area_skm (58.193) 与 GDAT.Area_Con (58.1) 的差异为 0.093，小于 10% 容差。\\n   - 平均水深：GDW.Depth_m (1.7) 与 GDAT.Avg_Depth (1.7) 完全一致。\\n5. 其他匹配：\\n   - 主要流域：GDW.Main_basin (\\\"Liao He\\\") 与 GDAT.Main_basin (\\\"Liao He\\\") 完全一致。\\n   - 国家：GDW.Country (\\\"China\\\") 与 GDAT.Admin0 (\\\"China\\\") 完全一致。\\n   - 行政单位：GDW.Admin_unit (\\\"Liaoning\\\") 与 GDAT.Admin2 (\\\"Kangping\\\") 不完全一致，但 GDW.NEAR_CITY (\\\"Kangping\\\") 与 GDAT.Admin2 (\\\"Kangping\\\") 一致，这提供了额外的地理关联。\\n6. 忽略元数据差异：\\n   - 数据来源和编辑者字段存在差异，但根据规则应忽略这些差异。\\n7. 综合评估：核心属性（大坝名称、河流名称、建造年份）完全一致，地理位置非常接近，关键物理属性在容差范围内一致，因此可以高度自信地判断这两条记录指向同一个物理大坝实体。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1417) 与 GDAT.Feature_ID (7544) 不同，但这是预期的，因为它们来自不同数据库。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name (\\\"Wolonghu\\\") 与 GDAT.Dam_Name (\\\"Wolonghu\\\") 完全一致。\\n   - 河流名称：GDW.River (\\\"Trib. Xiushui He\\\") 与 GDAT.River (\\\"Trib. Xiushui He\\\") 完全一致。\\n   - 建造年份：GDW.Year_dam (1958) 与 GDAT.Year_Fin (1958) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv (123.304822) 与 GDAT.Long (123.3044444) 差异为 0.0003776，非常接近。\\n   - 纬度：GDW.Lat_riv (42.73307) 与 GDAT.Lat (42.73277778) 差异为 0.00029222，非常接近。\\n4. 物理属性容差：\\n   - 水库容量：GDW.Cap_mcm (100.0) 与 GDAT.Volume_Con (100.0) 完全一致。\\n   - 水库面积：GDW.Area_skm (58.193) 与 GDAT.Area_Con (58.1) 的差异为 0.093，小于 10% 容差。\\n   - 平均水深：GDW.Depth_m (1.7) 与 GDAT.Avg_Depth (1.7) 完全一致。\\n5. 其他匹配：\\n   - 主要流域：GDW.Main_basin (\\\"Liao He\\\") 与 GDAT.Main_basin (\\\"Liao He\\\") 完全一致。\\n   - 国家：GDW.Country (\\\"China\\\") 与 GDAT.Admin0 (\\\"China\\\") 完全一致。\\n   - 行政单位：GDW.Admin_unit (\\\"Liaoning\\\") 与 GDAT.Admin2 (\\\"Kangping\\\") 不完全一致，但 GDW.NEAR_CITY (\\\"Kangping\\\") 与 GDAT.Admin2 (\\\"Kangping\\\") 一致，这提供了额外的地理关联。\\n6. 忽略元数据差异：\\n   - 数据来源和编辑者字段存在差异，但根据规则应忽略这些差异。\\n7. 综合评估：核心属性（大坝名称、河流名称、建造年份）完全一致，地理位置非常接近，关键物理属性在容差范围内一致，因此可以高度自信地判断这两条记录指向同一个物理大坝实体。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1421, 'RES_NAME': None, 'DAM_NAME': 'Santaizi', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': None, 'ALT_RIVER': None, 'MAIN_BASIN': 'Liao He', 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Liaoning', 'SEC_ADMIN': None, 'NEAR_CITY': 'Kangping', 'ALT_CITY': None, 'YEAR_DAM': 1944, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': -99, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1944', 'DAM_HGT_M': -99, 'ALT_HGT_M': -99, 'DAM_LEN_M': -99, 'ALT_LEN_M': -99, 'AREA_SKM': 15.032, 'AREA_POLY': 15.032, 'AREA_REP': 14.0, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 30.0, 'CAP_MAX': -99.0, 'CAP_REP': 30.0, 'CAP_MIN': 10.0, 'DEPTH_M': 2.0, 'DIS_AVG_LS': 3297, 'DOR_PC': 28.9, 'ELEV_MASL': 82, 'CATCH_SKM': 1425, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': None, 'USE_ELEC': None, 'USE_SUPP': None, 'USE_FCON': None, 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': None, 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '3: Fair', 'EDITOR': 'McGill-BL', 'LONG_RIV': 123.346382, 'LAT_RIV': 42.632437, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5853, 'HYRIV_ID': 40269409, 'INSTREAM': 'Instream', 'HYLAK_ID': 14401, 'HYBAS_L12': 4120360330}, \n",
      "记录2: {'Feature_ID': 7540, 'Dam_Name': 'Santaizi', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Liao He', 'Alt_River': None, 'Main_basin': 'Liao He', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': None, 'Admin2': 'Kangping', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': None, 'Seco_Admin': None, 'Year_Fin': '1944', 'Year_Const': None, 'Alt_Yr_Fin': None, 'Height': None, 'Alt_Height': None, 'Length': None, 'Alt_Length': None, 'Volume_Con': 30.0, 'Volume_Rep': 30.0, 'Volume_Max': 30.0, 'Volume_Min': '10', 'Area_Con': 15.0, 'Area_Pol': 15.0, 'Area_Rep': '14', 'Area_Max': '15', 'Area_Min': '14', 'Avg_Depth': 2.0, 'Sediment_r': nan, 'P_Irrig': nan, 'P_Hydro': nan, 'P_WSupp': None, 'P_FCont': None, 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Unspecified purpose', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 42.63277778, 'Long': 123.3461111, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Other'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=1421 与 GDAT.Feature_ID=7540 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称完全一致：Santaizi\\n   - 河流名称一致：Liao He\\n   - 建造年份一致：1944\\n3. 地理位置：\\n   - 经度：GDW=123.346382 vs GDAT=123.3461111（差值0.000271）\\n   - 纬度：GDW=42.632437 vs GDAT=42.63277778（差值0.0003408）\\n   - 坐标差值小于0.0005，属于合理误差范围\\n4. 物理属性：\\n   - 水库容量：GDW=30.0 vs GDAT=30.0（完全一致）\\n   - 水库面积：GDW=15.032 vs GDAT=15.0（误差0.21%）\\n   - 平均水深：GDW=2.0 vs GDAT=2.0（完全一致）\\n5. 其他匹配项：\\n   - 主要流域：Liao He\\n   - 国家：China\\n   - 行政单位：Liaoning (GDW) vs Kangping (GDAT Admin2)\\n   - 数据来源均包含GRanD\\n6. 不一致但可接受的差异：\\n   - GDW中DAM_HGT_M=-99（缺失）与GDAT中Height=null（缺失）\\n   - GDW中DAM_LEN_M=-99（缺失）与GDAT中Length=null（缺失）\\n   - GDW中ADMIN_UNIT=Liaoning 与 GDAT中Admin1=null, Admin2=Kangping（属于同一行政区域的不同层级）\\n   - GDAT中Main_P=Unspecified purpose 与 GDW中MAIN_USE=null（表示相同含义）\\n7. 综合评估：核心属性完全匹配，地理位置高度一致，物理属性在误差范围内，所有不一致项均为可接受的差异。因此判断为同一实体。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID=1421 与 GDAT.Feature_ID=7540 无直接关联，但核心属性匹配良好。\\n2. 核心属性比对：\\n   - 大坝名称完全一致：Santaizi\\n   - 河流名称一致：Liao He\\n   - 建造年份一致：1944\\n3. 地理位置：\\n   - 经度：GDW=123.346382 vs GDAT=123.3461111（差值0.000271）\\n   - 纬度：GDW=42.632437 vs GDAT=42.63277778（差值0.0003408）\\n   - 坐标差值小于0.0005，属于合理误差范围\\n4. 物理属性：\\n   - 水库容量：GDW=30.0 vs GDAT=30.0（完全一致）\\n   - 水库面积：GDW=15.032 vs GDAT=15.0（误差0.21%）\\n   - 平均水深：GDW=2.0 vs GDAT=2.0（完全一致）\\n5. 其他匹配项：\\n   - 主要流域：Liao He\\n   - 国家：China\\n   - 行政单位：Liaoning (GDW) vs Kangping (GDAT Admin2)\\n   - 数据来源均包含GRanD\\n6. 不一致但可接受的差异：\\n   - GDW中DAM_HGT_M=-99（缺失）与GDAT中Height=null（缺失）\\n   - GDW中DAM_LEN_M=-99（缺失）与GDAT中Length=null（缺失）\\n   - GDW中ADMIN_UNIT=Liaoning 与 GDAT中Admin1=null, Admin2=Kangping（属于同一行政区域的不同层级）\\n   - GDAT中Main_P=Unspecified purpose 与 GDW中MAIN_USE=null（表示相同含义）\\n7. 综合评估：核心属性完全匹配，地理位置高度一致，物理属性在误差范围内，所有不一致项均为可接受的差异。因此判断为同一实体。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1446, 'RES_NAME': None, 'DAM_NAME': 'Baishi', 'ALT_NAME': None, 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Daling He', 'ALT_RIVER': None, 'MAIN_BASIN': None, 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': None, 'ADMIN_UNIT': 'Liaoning', 'SEC_ADMIN': None, 'NEAR_CITY': 'Yixian', 'ALT_CITY': None, 'YEAR_DAM': 2001, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': -99, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 2001', 'DAM_HGT_M': 50, 'ALT_HGT_M': -99, 'DAM_LEN_M': 513, 'ALT_LEN_M': -99, 'AREA_SKM': 10.088, 'AREA_POLY': 10.088, 'AREA_REP': 79.5, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 1645.0, 'CAP_MAX': -99.0, 'CAP_REP': 1645.0, 'CAP_MIN': -99.0, 'DEPTH_M': 163.1, 'DIS_AVG_LS': 31466, 'DOR_PC': 165.8, 'ELEV_MASL': 96, 'CATCH_SKM': 17559, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': None, 'USE_ELEC': None, 'USE_SUPP': None, 'USE_FCON': None, 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': None, 'MULTI_DAMS': None, 'COMMENTS': None, 'URL': None, 'QUALITY': '4: Poor', 'EDITOR': 'McGill-BL', 'LONG_RIV': 121.004766, 'LAT_RIV': 41.678513, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'UY', 'GRAND_ID': 5878, 'HYRIV_ID': 40300826, 'INSTREAM': 'Instream', 'HYLAK_ID': 14447, 'HYBAS_L12': 4120395970}, \n",
      "记录2: {'Feature_ID': 7521, 'Dam_Name': 'Baishi (Liaoning)', 'Alt_Name': None, 'Reservoir': 'Baishi', 'Alt_Reserv': None, 'River': 'Daling He', 'Alt_River': None, 'Main_basin': None, 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': 'Liaoning', 'Admin2': 'Yixian', 'Admin3': None, 'Sec_Admin0': None, 'Sec_Admin1': None, 'Sec_Admin2': None, 'Seco_Admin': None, 'Year_Fin': '2001', 'Year_Const': None, 'Alt_Yr_Fin': None, 'Height': '50', 'Alt_Height': None, 'Length': '513', 'Alt_Length': None, 'Volume_Con': 1645.0, 'Volume_Rep': 1645.0, 'Volume_Max': 1645.0, 'Volume_Min': None, 'Area_Con': 10.1, 'Area_Pol': 10.1, 'Area_Rep': '79.5', 'Area_Max': '79.5', 'Area_Min': '10.1', 'Avg_Depth': 162.9, 'Sediment_r': nan, 'P_Irrig': nan, 'P_Hydro': nan, 'P_WSupp': None, 'P_FCont': None, 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Unspecified purpose', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 41.67805556, 'Long': 121.0047222, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Other'}\n",
      "{\"is_consistent\": true, \"confidence_score\": 0.95, \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1446) 与 GDAT.Feature_ID (7521) 无直接关联，但这是正常情况，因为不同数据库的ID通常不一致。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name (\\\"Baishi\\\") 与 GDAT.Dam_Name (\\\"Baishi (Liaoning)\\\") 基本一致，仅行政区划信息差异。\\n   - 河流名称：GDW.River (\\\"Daling He\\\") 与 GDAT.River (\\\"Daling He\\\") 完全一致。\\n   - 建造年份：GDW.Year_dam (2001) 与 GDAT.Year_Fin (2001) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv (121.004766) 与 GDAT.Long (121.0047222) 差异极小（0.0000438）。\\n   - 纬度：GDW.Lat_riv (41.678513) 与 GDAT.Lat (41.67805556) 差异极小（0.00045744）。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m (50) 与 GDAT.Height (50) 完全一致。\\n   - 水库容量：GDW.Cap_mcm (1645.0) 与 GDAT.Volume_Con (1645.0) 完全一致。\\n   - 水库面积：GDW.Area_skm (10.088) 与 GDAT.Area_Con (10.1) 在容差范围内（误差0.012，约0.12%）。\\n   - 平均水深：GDW.Depth_m (163.1) 与 GDAT.Avg_Depth (162.9) 在容差范围内（误差0.2，约0.12%）。\\n5. 其他信息：\\n   - 国家和行政单位：GDW.Country (\\\"China\\\") 与 GDAT.Admin0 (\\\"China\\\") 一致；GDW.Admin_unit (\\\"Liaoning\\\") 与 GDAT.Admin1 (\\\"Liaoning\\\") 一致。\\n   - 数据来源：GDW.Orig_src (\\\"GRanD\\\") 与 GDAT.Source (\\\"AQUASTAT, GRanD\\\") 有重叠。\\n综合以上所有匹配项，尤其是核心属性和物理属性的高度一致，可以确定这两条记录指向同一个物理大坝实体。\"}\n",
      "{\n",
      "  \"is_consistent\": true,\n",
      "  \"confidence_score\": 0.95,\n",
      "  \"reasoning\": \"1. 核心标识符：GDW.GDW_ID (1446) 与 GDAT.Feature_ID (7521) 无直接关联，但这是正常情况，因为不同数据库的ID通常不一致。\\n2. 核心属性匹配：\\n   - 大坝名称：GDW.Dam_name (\\\"Baishi\\\") 与 GDAT.Dam_Name (\\\"Baishi (Liaoning)\\\") 基本一致，仅行政区划信息差异。\\n   - 河流名称：GDW.River (\\\"Daling He\\\") 与 GDAT.River (\\\"Daling He\\\") 完全一致。\\n   - 建造年份：GDW.Year_dam (2001) 与 GDAT.Year_Fin (2001) 完全一致。\\n3. 地理位置：\\n   - 经度：GDW.Long_riv (121.004766) 与 GDAT.Long (121.0047222) 差异极小（0.0000438）。\\n   - 纬度：GDW.Lat_riv (41.678513) 与 GDAT.Lat (41.67805556) 差异极小（0.00045744）。\\n4. 物理属性容差：\\n   - 大坝高度：GDW.Dam_hgt_m (50) 与 GDAT.Height (50) 完全一致。\\n   - 水库容量：GDW.Cap_mcm (1645.0) 与 GDAT.Volume_Con (1645.0) 完全一致。\\n   - 水库面积：GDW.Area_skm (10.088) 与 GDAT.Area_Con (10.1) 在容差范围内（误差0.012，约0.12%）。\\n   - 平均水深：GDW.Depth_m (163.1) 与 GDAT.Avg_Depth (162.9) 在容差范围内（误差0.2，约0.12%）。\\n5. 其他信息：\\n   - 国家和行政单位：GDW.Country (\\\"China\\\") 与 GDAT.Admin0 (\\\"China\\\") 一致；GDW.Admin_unit (\\\"Liaoning\\\") 与 GDAT.Admin1 (\\\"Liaoning\\\") 一致。\\n   - 数据来源：GDW.Orig_src (\\\"GRanD\\\") 与 GDAT.Source (\\\"AQUASTAT, GRanD\\\") 有重叠。\\n综合以上所有匹配项，尤其是核心属性和物理属性的高度一致，可以确定这两条记录指向同一个物理大坝实体。\"\n",
      "}\n",
      "记录1: {'GDW_ID': 1455, 'RES_NAME': None, 'DAM_NAME': 'Yunfeng', 'ALT_NAME': 'Undong', 'DAM_TYPE': 'Dam', 'LAKE_CTRL': None, 'RIVER': 'Yalu Jiang', 'ALT_RIVER': 'Amnokgang', 'MAIN_BASIN': 'Yalu Jiang', 'SUB_BASIN': None, 'COUNTRY': 'China', 'SEC_CNTRY': 'North Korea', 'ADMIN_UNIT': 'Jilin', 'SEC_ADMIN': 'Chagang-do', 'NEAR_CITY': 'Jian', 'ALT_CITY': 'Chasong', 'YEAR_DAM': 1971, 'PRE_YEAR': -99, 'YEAR_SRC': 'GRanD', 'ALT_YEAR': 1966, 'REM_YEAR': -99, 'TIMELINE': None, 'YEAR_TXT': 'Built 1971', 'DAM_HGT_M': 114, 'ALT_HGT_M': -99, 'DAM_LEN_M': 828, 'ALT_LEN_M': -99, 'AREA_SKM': 45.994, 'AREA_POLY': 45.994, 'AREA_REP': 10.6, 'AREA_MAX': -99.0, 'AREA_MIN': -99.0, 'CAP_MCM': 3911.0, 'CAP_MAX': -99.0, 'CAP_REP': 3911.0, 'CAP_MIN': 3708.0, 'DEPTH_M': 85.0, 'DIS_AVG_LS': 191807, 'DOR_PC': 64.7, 'ELEV_MASL': 283, 'CATCH_SKM': 24206, 'CATCH_REP': -99, 'POWER_MW': -99, 'DATA_INFO': None, 'USE_IRRI': None, 'USE_ELEC': 'Main', 'USE_SUPP': None, 'USE_FCON': None, 'USE_RECR': None, 'USE_NAVI': None, 'USE_FISH': None, 'USE_PCON': None, 'USE_LIVE': None, 'USE_OTHR': None, 'MAIN_USE': 'Hydroelectricity', 'MULTI_DAMS': None, 'COMMENTS': 'Alternative year 1970', 'URL': None, 'QUALITY': '3: Fair', 'EDITOR': 'McGill-BL', 'LONG_RIV': 126.513495, 'LAT_RIV': 41.382039, 'LONG_DAM': 0.0, 'LAT_DAM': 0.0, 'ORIG_SRC': 'GRanD', 'POLY_SRC': 'SWBD', 'GRAND_ID': 5882, 'HYRIV_ID': 40309836, 'INSTREAM': 'Instream', 'HYLAK_ID': 14464, 'HYBAS_L12': 4120406370}, \n",
      "记录2: {'Feature_ID': 6824, 'Dam_Name': 'Yunfeng', 'Alt_Name': None, 'Reservoir': None, 'Alt_Reserv': None, 'River': 'Yalu Jiang', 'Alt_River': 'Amnok', 'Main_basin': 'Yalu Jiang', 'Sub_basin': None, 'Continent': 'Asia', 'Admin0': 'China', 'Admin1': None, 'Admin2': 'Jian', 'Admin3': None, 'Sec_Admin0': 'North Korea', 'Sec_Admin1': 'Chagangdo', 'Sec_Admin2': 'Chasong', 'Seco_Admin': None, 'Year_Fin': '1970', 'Year_Const': None, 'Alt_Yr_Fin': '1966, 1971', 'Height': '114', 'Alt_Height': None, 'Length': '828', 'Alt_Length': None, 'Volume_Con': 3911.0, 'Volume_Rep': 3911.0, 'Volume_Max': 3911.0, 'Volume_Min': '3708', 'Area_Con': 45.9, 'Area_Pol': 45.9, 'Area_Rep': '10.6', 'Area_Max': '45.9', 'Area_Min': '10.6', 'Avg_Depth': 85.2, 'Sediment_r': nan, 'P_Irrig': nan, 'P_Hydro': 1.0, 'P_WSupp': None, 'P_FCont': None, 'P_Navig': nan, 'P_Fishr': nan, 'P_Livst': nan, 'P_Recrn': nan, 'P_PCont': nan, 'P_WStor': nan, 'P_Other': nan, 'Main_P': 'Hydroelectricity', 'Sec_P': None, 'Mult_Dams': None, 'Timeline': None, 'Lat': 41.38277778, 'Long': 126.5127778, 'Flag_Qual': '1', 'Capac': nan, 'Alt_Capac': nan, 'Source': 'AQUASTAT, GRanD', 'Comment': None, 'Editor': 'Xinyi', 'Main_P_Map': 'Hydroelectricity'}\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[36], line 46\u001b[0m\n\u001b[0;32m     41\u001b[0m \u001b[38;5;66;03m# --- 修改结束 ---\u001b[39;00m\n\u001b[0;32m     42\u001b[0m \n\u001b[0;32m     43\u001b[0m \u001b[38;5;66;03m# 现在 record_a 和 record_b 都是干净的、可序列化的字典了\u001b[39;00m\n\u001b[0;32m     44\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m记录1: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrecord_a\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m记录2: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrecord_b\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 46\u001b[0m analysis_result \u001b[38;5;241m=\u001b[39m are_records_consistent_advanced(record1 \u001b[38;5;241m=\u001b[39m record_a, record2 \u001b[38;5;241m=\u001b[39m record_b)\n\u001b[0;32m     47\u001b[0m \u001b[38;5;28mprint\u001b[39m(json\u001b[38;5;241m.\u001b[39mdumps(analysis_result, indent\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m, ensure_ascii\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n",
      "Cell \u001b[1;32mIn[35], line 19\u001b[0m, in \u001b[0;36mare_records_consistent_advanced\u001b[1;34m(record1, record2, model)\u001b[0m\n\u001b[0;32m     16\u001b[0m prompt \u001b[38;5;241m=\u001b[39m prompt_template\u001b[38;5;241m.\u001b[39mformat(record1\u001b[38;5;241m=\u001b[39mrecord1_str, record2\u001b[38;5;241m=\u001b[39mrecord2_str)\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 19\u001b[0m     response \u001b[38;5;241m=\u001b[39m client\u001b[38;5;241m.\u001b[39mchat\u001b[38;5;241m.\u001b[39mcompletions\u001b[38;5;241m.\u001b[39mcreate(\n\u001b[0;32m     20\u001b[0m         model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[0;32m     21\u001b[0m         messages\u001b[38;5;241m=\u001b[39m[{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: prompt}],\n\u001b[0;32m     22\u001b[0m         \u001b[38;5;66;03m# 开启JSON模式，确保模型输出为有效的JSON\u001b[39;00m\n\u001b[0;32m     23\u001b[0m         response_format\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjson_object\u001b[39m\u001b[38;5;124m\"\u001b[39m}, \n\u001b[0;32m     24\u001b[0m         temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m\n\u001b[0;32m     25\u001b[0m     )\n\u001b[0;32m     27\u001b[0m     response_content \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mchoices[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmessage\u001b[38;5;241m.\u001b[39mcontent\n\u001b[0;32m     29\u001b[0m     \u001b[38;5;28mprint\u001b[39m(response_content)\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\openai\\_utils\\_utils.py:286\u001b[0m, in \u001b[0;36mrequired_args.<locals>.inner.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    284\u001b[0m             msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing required argument: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mquote(missing[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    285\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(msg)\n\u001b[1;32m--> 286\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\openai\\resources\\chat\\completions\\completions.py:1147\u001b[0m, in \u001b[0;36mCompletions.create\u001b[1;34m(self, messages, model, audio, frequency_penalty, function_call, functions, logit_bias, logprobs, max_completion_tokens, max_tokens, metadata, modalities, n, parallel_tool_calls, prediction, presence_penalty, prompt_cache_key, reasoning_effort, response_format, safety_identifier, seed, service_tier, stop, store, stream, stream_options, temperature, tool_choice, tools, top_logprobs, top_p, user, verbosity, web_search_options, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[0;32m   1101\u001b[0m \u001b[38;5;129m@required_args\u001b[39m([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m], [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m   1102\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[0;32m   1103\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1144\u001b[0m     timeout: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m httpx\u001b[38;5;241m.\u001b[39mTimeout \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m|\u001b[39m NotGiven \u001b[38;5;241m=\u001b[39m NOT_GIVEN,\n\u001b[0;32m   1145\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ChatCompletion \u001b[38;5;241m|\u001b[39m Stream[ChatCompletionChunk]:\n\u001b[0;32m   1146\u001b[0m     validate_response_format(response_format)\n\u001b[1;32m-> 1147\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_post(\n\u001b[0;32m   1148\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/chat/completions\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1149\u001b[0m         body\u001b[38;5;241m=\u001b[39mmaybe_transform(\n\u001b[0;32m   1150\u001b[0m             {\n\u001b[0;32m   1151\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmessages\u001b[39m\u001b[38;5;124m\"\u001b[39m: messages,\n\u001b[0;32m   1152\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m: model,\n\u001b[0;32m   1153\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maudio\u001b[39m\u001b[38;5;124m\"\u001b[39m: audio,\n\u001b[0;32m   1154\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrequency_penalty\u001b[39m\u001b[38;5;124m\"\u001b[39m: frequency_penalty,\n\u001b[0;32m   1155\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunction_call\u001b[39m\u001b[38;5;124m\"\u001b[39m: function_call,\n\u001b[0;32m   1156\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunctions\u001b[39m\u001b[38;5;124m\"\u001b[39m: functions,\n\u001b[0;32m   1157\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogit_bias\u001b[39m\u001b[38;5;124m\"\u001b[39m: logit_bias,\n\u001b[0;32m   1158\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogprobs\u001b[39m\u001b[38;5;124m\"\u001b[39m: logprobs,\n\u001b[0;32m   1159\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_completion_tokens\u001b[39m\u001b[38;5;124m\"\u001b[39m: max_completion_tokens,\n\u001b[0;32m   1160\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmax_tokens\u001b[39m\u001b[38;5;124m\"\u001b[39m: max_tokens,\n\u001b[0;32m   1161\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmetadata\u001b[39m\u001b[38;5;124m\"\u001b[39m: metadata,\n\u001b[0;32m   1162\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodalities\u001b[39m\u001b[38;5;124m\"\u001b[39m: modalities,\n\u001b[0;32m   1163\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn\u001b[39m\u001b[38;5;124m\"\u001b[39m: n,\n\u001b[0;32m   1164\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparallel_tool_calls\u001b[39m\u001b[38;5;124m\"\u001b[39m: parallel_tool_calls,\n\u001b[0;32m   1165\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprediction\u001b[39m\u001b[38;5;124m\"\u001b[39m: prediction,\n\u001b[0;32m   1166\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpresence_penalty\u001b[39m\u001b[38;5;124m\"\u001b[39m: presence_penalty,\n\u001b[0;32m   1167\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprompt_cache_key\u001b[39m\u001b[38;5;124m\"\u001b[39m: prompt_cache_key,\n\u001b[0;32m   1168\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreasoning_effort\u001b[39m\u001b[38;5;124m\"\u001b[39m: reasoning_effort,\n\u001b[0;32m   1169\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse_format\u001b[39m\u001b[38;5;124m\"\u001b[39m: response_format,\n\u001b[0;32m   1170\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msafety_identifier\u001b[39m\u001b[38;5;124m\"\u001b[39m: safety_identifier,\n\u001b[0;32m   1171\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mseed\u001b[39m\u001b[38;5;124m\"\u001b[39m: seed,\n\u001b[0;32m   1172\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mservice_tier\u001b[39m\u001b[38;5;124m\"\u001b[39m: service_tier,\n\u001b[0;32m   1173\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstop\u001b[39m\u001b[38;5;124m\"\u001b[39m: stop,\n\u001b[0;32m   1174\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstore\u001b[39m\u001b[38;5;124m\"\u001b[39m: store,\n\u001b[0;32m   1175\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream\u001b[39m\u001b[38;5;124m\"\u001b[39m: stream,\n\u001b[0;32m   1176\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstream_options\u001b[39m\u001b[38;5;124m\"\u001b[39m: stream_options,\n\u001b[0;32m   1177\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtemperature\u001b[39m\u001b[38;5;124m\"\u001b[39m: temperature,\n\u001b[0;32m   1178\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtool_choice\u001b[39m\u001b[38;5;124m\"\u001b[39m: tool_choice,\n\u001b[0;32m   1179\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtools\u001b[39m\u001b[38;5;124m\"\u001b[39m: tools,\n\u001b[0;32m   1180\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtop_logprobs\u001b[39m\u001b[38;5;124m\"\u001b[39m: top_logprobs,\n\u001b[0;32m   1181\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtop_p\u001b[39m\u001b[38;5;124m\"\u001b[39m: top_p,\n\u001b[0;32m   1182\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m: user,\n\u001b[0;32m   1183\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mverbosity\u001b[39m\u001b[38;5;124m\"\u001b[39m: verbosity,\n\u001b[0;32m   1184\u001b[0m                 \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweb_search_options\u001b[39m\u001b[38;5;124m\"\u001b[39m: web_search_options,\n\u001b[0;32m   1185\u001b[0m             },\n\u001b[0;32m   1186\u001b[0m             completion_create_params\u001b[38;5;241m.\u001b[39mCompletionCreateParamsStreaming\n\u001b[0;32m   1187\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m stream\n\u001b[0;32m   1188\u001b[0m             \u001b[38;5;28;01melse\u001b[39;00m completion_create_params\u001b[38;5;241m.\u001b[39mCompletionCreateParamsNonStreaming,\n\u001b[0;32m   1189\u001b[0m         ),\n\u001b[0;32m   1190\u001b[0m         options\u001b[38;5;241m=\u001b[39mmake_request_options(\n\u001b[0;32m   1191\u001b[0m             extra_headers\u001b[38;5;241m=\u001b[39mextra_headers, extra_query\u001b[38;5;241m=\u001b[39mextra_query, extra_body\u001b[38;5;241m=\u001b[39mextra_body, timeout\u001b[38;5;241m=\u001b[39mtimeout\n\u001b[0;32m   1192\u001b[0m         ),\n\u001b[0;32m   1193\u001b[0m         cast_to\u001b[38;5;241m=\u001b[39mChatCompletion,\n\u001b[0;32m   1194\u001b[0m         stream\u001b[38;5;241m=\u001b[39mstream \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1195\u001b[0m         stream_cls\u001b[38;5;241m=\u001b[39mStream[ChatCompletionChunk],\n\u001b[0;32m   1196\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\openai\\_base_client.py:1259\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[1;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[0;32m   1245\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[0;32m   1246\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   1247\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1254\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   1255\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m   1256\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[0;32m   1257\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[0;32m   1258\u001b[0m     )\n\u001b[1;32m-> 1259\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequest(cast_to, opts, stream\u001b[38;5;241m=\u001b[39mstream, stream_cls\u001b[38;5;241m=\u001b[39mstream_cls))\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\openai\\_base_client.py:982\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[1;34m(self, cast_to, options, stream, stream_cls)\u001b[0m\n\u001b[0;32m    980\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    981\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 982\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_client\u001b[38;5;241m.\u001b[39msend(\n\u001b[0;32m    983\u001b[0m         request,\n\u001b[0;32m    984\u001b[0m         stream\u001b[38;5;241m=\u001b[39mstream \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_stream_response_body(request\u001b[38;5;241m=\u001b[39mrequest),\n\u001b[0;32m    985\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    986\u001b[0m     )\n\u001b[0;32m    987\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m httpx\u001b[38;5;241m.\u001b[39mTimeoutException \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m    988\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEncountered httpx.TimeoutException\u001b[39m\u001b[38;5;124m\"\u001b[39m, exc_info\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpx\\_client.py:914\u001b[0m, in \u001b[0;36mClient.send\u001b[1;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[0;32m    906\u001b[0m follow_redirects \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    907\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollow_redirects\n\u001b[0;32m    908\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(follow_redirects, UseClientDefault)\n\u001b[0;32m    909\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m follow_redirects\n\u001b[0;32m    910\u001b[0m )\n\u001b[0;32m    912\u001b[0m auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_request_auth(request, auth)\n\u001b[1;32m--> 914\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_handling_auth(\n\u001b[0;32m    915\u001b[0m     request,\n\u001b[0;32m    916\u001b[0m     auth\u001b[38;5;241m=\u001b[39mauth,\n\u001b[0;32m    917\u001b[0m     follow_redirects\u001b[38;5;241m=\u001b[39mfollow_redirects,\n\u001b[0;32m    918\u001b[0m     history\u001b[38;5;241m=\u001b[39m[],\n\u001b[0;32m    919\u001b[0m )\n\u001b[0;32m    920\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    921\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpx\\_client.py:942\u001b[0m, in \u001b[0;36mClient._send_handling_auth\u001b[1;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[0;32m    939\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[0;32m    941\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 942\u001b[0m     response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_handling_redirects(\n\u001b[0;32m    943\u001b[0m         request,\n\u001b[0;32m    944\u001b[0m         follow_redirects\u001b[38;5;241m=\u001b[39mfollow_redirects,\n\u001b[0;32m    945\u001b[0m         history\u001b[38;5;241m=\u001b[39mhistory,\n\u001b[0;32m    946\u001b[0m     )\n\u001b[0;32m    947\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    948\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpx\\_client.py:979\u001b[0m, in \u001b[0;36mClient._send_handling_redirects\u001b[1;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[0;32m    976\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequest\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m    977\u001b[0m     hook(request)\n\u001b[1;32m--> 979\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_single_request(request)\n\u001b[0;32m    980\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    981\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpx\\_client.py:1015\u001b[0m, in \u001b[0;36mClient._send_single_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m   1010\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m   1011\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1012\u001b[0m     )\n\u001b[0;32m   1014\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request\u001b[38;5;241m=\u001b[39mrequest):\n\u001b[1;32m-> 1015\u001b[0m     response \u001b[38;5;241m=\u001b[39m transport\u001b[38;5;241m.\u001b[39mhandle_request(request)\n\u001b[0;32m   1017\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[0;32m   1019\u001b[0m response\u001b[38;5;241m.\u001b[39mrequest \u001b[38;5;241m=\u001b[39m request\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpx\\_transports\\default.py:233\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m    220\u001b[0m req \u001b[38;5;241m=\u001b[39m httpcore\u001b[38;5;241m.\u001b[39mRequest(\n\u001b[0;32m    221\u001b[0m     method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m    222\u001b[0m     url\u001b[38;5;241m=\u001b[39mhttpcore\u001b[38;5;241m.\u001b[39mURL(\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    230\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[0;32m    231\u001b[0m )\n\u001b[0;32m    232\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[1;32m--> 233\u001b[0m     resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pool\u001b[38;5;241m.\u001b[39mhandle_request(req)\n\u001b[0;32m    235\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m    237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[0;32m    238\u001b[0m     status_code\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mstatus,\n\u001b[0;32m    239\u001b[0m     headers\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m    240\u001b[0m     stream\u001b[38;5;241m=\u001b[39mResponseStream(resp\u001b[38;5;241m.\u001b[39mstream),\n\u001b[0;32m    241\u001b[0m     extensions\u001b[38;5;241m=\u001b[39mresp\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[0;32m    242\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_sync\\connection_pool.py:268\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m    266\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ShieldCancellation():\n\u001b[0;32m    267\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresponse_closed(status)\n\u001b[1;32m--> 268\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m    269\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    270\u001b[0m     \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_sync\\connection_pool.py:251\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m    248\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m    250\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 251\u001b[0m     response \u001b[38;5;241m=\u001b[39m connection\u001b[38;5;241m.\u001b[39mhandle_request(request)\n\u001b[0;32m    252\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[0;32m    253\u001b[0m     \u001b[38;5;66;03m# The ConnectionNotAvailable exception is a special case, that\u001b[39;00m\n\u001b[0;32m    254\u001b[0m     \u001b[38;5;66;03m# indicates we need to retry the request on a new connection.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[38;5;66;03m# might end up as an HTTP/2 connection, but which actually ends\u001b[39;00m\n\u001b[0;32m    259\u001b[0m     \u001b[38;5;66;03m# up as HTTP/1.1.\u001b[39;00m\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pool_lock:\n\u001b[0;32m    261\u001b[0m         \u001b[38;5;66;03m# Maintain our position in the request queue, but reset the\u001b[39;00m\n\u001b[0;32m    262\u001b[0m         \u001b[38;5;66;03m# status so that the request becomes queued again.\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_sync\\connection.py:103\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mis_available():\n\u001b[0;32m    101\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectionNotAvailable()\n\u001b[1;32m--> 103\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mhandle_request(request)\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_sync\\http11.py:133\u001b[0m, in \u001b[0;36mHTTP11Connection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m    131\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse_closed\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[0;32m    132\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_response_closed()\n\u001b[1;32m--> 133\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_sync\\http11.py:111\u001b[0m, in \u001b[0;36mHTTP11Connection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m    101\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\n\u001b[0;32m    104\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mreceive_response_headers\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request, kwargs\n\u001b[0;32m    105\u001b[0m ) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[0;32m    106\u001b[0m     (\n\u001b[0;32m    107\u001b[0m         http_version,\n\u001b[0;32m    108\u001b[0m         status,\n\u001b[0;32m    109\u001b[0m         reason_phrase,\n\u001b[0;32m    110\u001b[0m         headers,\n\u001b[1;32m--> 111\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_receive_response_headers(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    112\u001b[0m     trace\u001b[38;5;241m.\u001b[39mreturn_value \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m    113\u001b[0m         http_version,\n\u001b[0;32m    114\u001b[0m         status,\n\u001b[0;32m    115\u001b[0m         reason_phrase,\n\u001b[0;32m    116\u001b[0m         headers,\n\u001b[0;32m    117\u001b[0m     )\n\u001b[0;32m    119\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Response(\n\u001b[0;32m    120\u001b[0m     status\u001b[38;5;241m=\u001b[39mstatus,\n\u001b[0;32m    121\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    127\u001b[0m     },\n\u001b[0;32m    128\u001b[0m )\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_sync\\http11.py:176\u001b[0m, in \u001b[0;36mHTTP11Connection._receive_response_headers\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m    173\u001b[0m timeout \u001b[38;5;241m=\u001b[39m timeouts\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mread\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 176\u001b[0m     event \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_receive_event(timeout\u001b[38;5;241m=\u001b[39mtimeout)\n\u001b[0;32m    177\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(event, h11\u001b[38;5;241m.\u001b[39mResponse):\n\u001b[0;32m    178\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_sync\\http11.py:212\u001b[0m, in \u001b[0;36mHTTP11Connection._receive_event\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    209\u001b[0m     event \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h11_state\u001b[38;5;241m.\u001b[39mnext_event()\n\u001b[0;32m    211\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event \u001b[38;5;129;01mis\u001b[39;00m h11\u001b[38;5;241m.\u001b[39mNEED_DATA:\n\u001b[1;32m--> 212\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_network_stream\u001b[38;5;241m.\u001b[39mread(\n\u001b[0;32m    213\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mREAD_NUM_BYTES, timeout\u001b[38;5;241m=\u001b[39mtimeout\n\u001b[0;32m    214\u001b[0m     )\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;66;03m# If we feed this case through h11 we'll raise an exception like:\u001b[39;00m\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m    218\u001b[0m     \u001b[38;5;66;03m#     httpcore.RemoteProtocolError: can't handle event type\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    222\u001b[0m     \u001b[38;5;66;03m# perspective. Instead we handle this case distinctly and treat\u001b[39;00m\n\u001b[0;32m    223\u001b[0m     \u001b[38;5;66;03m# it as a ConnectError.\u001b[39;00m\n\u001b[0;32m    224\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;241m==\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_h11_state\u001b[38;5;241m.\u001b[39mtheir_state \u001b[38;5;241m==\u001b[39m h11\u001b[38;5;241m.\u001b[39mSEND_RESPONSE:\n",
      "File \u001b[1;32mc:\\Users\\lijunjie\\AppData\\Local\\ESRI\\conda\\envs\\arcgispro-py3-clone\\Lib\\site-packages\\httpcore\\_backends\\sync.py:126\u001b[0m, in \u001b[0;36mSyncStream.read\u001b[1;34m(self, max_bytes, timeout)\u001b[0m\n\u001b[0;32m    124\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[0;32m    125\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39msettimeout(timeout)\n\u001b[1;32m--> 126\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sock\u001b[38;5;241m.\u001b[39mrecv(max_bytes)\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 定义容差\n",
    "import numpy as np\n",
    "tolerance = 0.001  # 容差范围，0.0001 度大约等于 10 米\n",
    "\n",
    "# 1. 提取坐标为 NumPy 数组\n",
    "coords1 = china_data[['LONG_RIV', 'LAT_RIV']].to_numpy()\n",
    "coords2 = china_data2[['Long', 'Lat']].to_numpy()\n",
    "\n",
    "# 2. 使用 NumPy 的广播机制计算所有坐标对的差值\n",
    "# coords1[:, np.newaxis, :] 的形状变为 (N, 1, 2)\n",
    "# coords2[np.newaxis, :, :] 的形状变为 (1, M, 2)\n",
    "# NumPy 会自动广播，计算出一个 (N, M, 2) 的差值矩阵\n",
    "diff = np.abs(coords1[:, np.newaxis, :] - coords2[np.newaxis, :, :])\n",
    "\n",
    "# 3. 找到经度和纬度差值都小于容差的配对\n",
    "# close_mask 是一个 (N, M) 的布尔矩阵\n",
    "close_mask = np.all(diff < tolerance, axis=2)\n",
    "\n",
    "# 4. 获取所有满足条件的配对的索引 (idx1, idx2)\n",
    "indices1, indices2 = np.where(close_mask)\n",
    "\n",
    "# 5. 根据索引创建坐标对\n",
    "common_coordinates = []\n",
    "for idx1, idx2 in zip(indices1, indices2):\n",
    "    row1 = china_data.iloc[idx1]\n",
    "    row2 = china_data2.iloc[idx2]\n",
    "    common_coordinates.append((row1, row2))\n",
    "\n",
    "print(f\"找到的匹配坐标对数量：{len(common_coordinates)}\")\n",
    "\n",
    "# 后续处理流程保持不变\n",
    "for record in common_coordinates:\n",
    "    record_a = record[0].to_dict()\n",
    "    record_b = record[1].to_dict()\n",
    "\n",
    "    # --- 开始修改 ---\n",
    "    # 移除无法被JSON序列化的'geometry'对象\n",
    "    # 使用 .pop(key, None) 可以在键不存在时不报错，代码更健壮\n",
    "    record_a.pop('geometry', None)\n",
    "    record_b.pop('geometry', None)\n",
    "    # --- 修改结束 ---\n",
    "\n",
    "    # 现在 record_a 和 record_b 都是干净的、可序列化的字典了\n",
    "    print(f\"记录1: {record_a}, \\n记录2: {record_b}\")\n",
    "    \n",
    "    analysis_result = are_records_consistent_advanced(record1 = record_a, record2 = record_b)\n",
    "    print(json.dumps(analysis_result, indent=2, ensure_ascii=False))\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e18840",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e021eb9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   GDW_ID         RES_NAME             DAM_NAME        ALT_NAME  \\\n",
      "0       1    Lake Winnipeg               Jenpeg      Split Lake   \n",
      "1       2          Ontario             Iroquois            None   \n",
      "2       3           Baikal              Irkutsk            None   \n",
      "3       4    Lake Victoria           Owen Falls            None   \n",
      "4       5  Southern Indian  Missi Falls Control  Notigi Control   \n",
      "\n",
      "           DAM_TYPE LAKE_CTRL         RIVER ALT_RIVER MAIN_BASIN  \\\n",
      "0  Lake Control Dam       Yes        Nelson      None       None   \n",
      "1  Lake Control Dam       Yes  St. Lawrence      None       None   \n",
      "2  Lake Control Dam       Yes        Angara      None       None   \n",
      "3  Lake Control Dam       Yes    White Nile      None       Nile   \n",
      "4  Lake Control Dam       Yes     Churchill       Rat       None   \n",
      "\n",
      "       SUB_BASIN  ... LONG_DAM LAT_DAM ORIG_SRC POLY_SRC GRAND_ID  HYRIV_ID  \\\n",
      "0           None  ...      0.0     0.0    GRanD   CanVec      709  70125969   \n",
      "1           None  ...      0.0     0.0    GRanD     SWBD     1485  70444883   \n",
      "2           None  ...      0.0     0.0    GRanD     SWBD     5058  30588837   \n",
      "3  Victoria Nile  ...      0.0     0.0    GRanD     SWBD     4492  10980811   \n",
      "4           None  ...      0.0     0.0    GRanD    Other      702  70037207   \n",
      "\n",
      "   INSTREAM  HYLAK_ID   HYBAS_L12  \\\n",
      "0  Instream         4  7120921060   \n",
      "1  Instream         7  7121021260   \n",
      "2  Instream        11  3120638840   \n",
      "3  Instream        16  1122078520   \n",
      "4  Instream        37  7120896070   \n",
      "\n",
      "                                            geometry  \n",
      "0  POLYGON ((-98.80636 53.88021, -98.80578 53.880...  \n",
      "1  POLYGON ((-79.09167 43.81213, -79.09133 43.812...  \n",
      "2  POLYGON ((109.74514 55.86611, 109.74541 55.865...  \n",
      "3  POLYGON ((31.91181 -2.72236, 31.91141 -2.72289...  \n",
      "4  POLYGON ((-98.19348 57.56911, -98.19312 57.569...  \n",
      "\n",
      "[5 rows x 72 columns]\n",
      "Index(['GDW_ID', 'RES_NAME', 'DAM_NAME', 'ALT_NAME', 'DAM_TYPE', 'LAKE_CTRL',\n",
      "       'RIVER', 'ALT_RIVER', 'MAIN_BASIN', 'SUB_BASIN', 'COUNTRY', 'SEC_CNTRY',\n",
      "       'ADMIN_UNIT', 'SEC_ADMIN', 'NEAR_CITY', 'ALT_CITY', 'YEAR_DAM',\n",
      "       'PRE_YEAR', 'YEAR_SRC', 'ALT_YEAR', 'REM_YEAR', 'TIMELINE', 'YEAR_TXT',\n",
      "       'DAM_HGT_M', 'ALT_HGT_M', 'DAM_LEN_M', 'ALT_LEN_M', 'AREA_SKM',\n",
      "       'AREA_POLY', 'AREA_REP', 'AREA_MAX', 'AREA_MIN', 'CAP_MCM', 'CAP_MAX',\n",
      "       'CAP_REP', 'CAP_MIN', 'DEPTH_M', 'DIS_AVG_LS', 'DOR_PC', 'ELEV_MASL',\n",
      "       'CATCH_SKM', 'CATCH_REP', 'POWER_MW', 'DATA_INFO', 'USE_IRRI',\n",
      "       'USE_ELEC', 'USE_SUPP', 'USE_FCON', 'USE_RECR', 'USE_NAVI', 'USE_FISH',\n",
      "       'USE_PCON', 'USE_LIVE', 'USE_OTHR', 'MAIN_USE', 'MULTI_DAMS',\n",
      "       'COMMENTS', 'URL', 'QUALITY', 'EDITOR', 'LONG_RIV', 'LAT_RIV',\n",
      "       'LONG_DAM', 'LAT_DAM', 'ORIG_SRC', 'POLY_SRC', 'GRAND_ID', 'HYRIV_ID',\n",
      "       'INSTREAM', 'HYLAK_ID', 'HYBAS_L12', 'geometry'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 读取GDW中国水库数据\n",
    "GDW_reser_shapefile_path = r\"X:\\ljj\\水库水电站数据\\全球\\GDW\\GDW_v1_0_shp\\GDW_v1_0_shp\\GDW_reservoirs_v1_0.shp\"\n",
    "gdf3 = gpd.read_file(GDW_reser_shapefile_path)\n",
    "print(gdf3.head())\n",
    "print(gdf3.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93561bc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "符合条件的中国数据条数：6542\n",
      "6542\n",
      "6542\n",
      "6542\n",
      "[(118.726991, 33.08953), (126.686668, 43.717337), (116.837049, 40.483149), (124.966211, 40.462665), (117.441094, 40.033694)]\n",
      "(118.726991, 33.08953)\n",
      "(126.686668, 43.717337)\n",
      "(116.837049, 40.483149)\n",
      "(124.966211, 40.462665)\n",
      "(117.441094, 40.033694)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lijunjie\\AppData\\Local\\Temp\\ipykernel_23652\\3253839742.py:18: FutureWarning: The geopandas.dataset module is deprecated and will be removed in GeoPandas 1.0. You can get the original 'naturalearth_lowres' data from https://www.naturalearthdata.com/downloads/110m-cultural-vectors/.\n",
      "  china_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 筛选中国数据\n",
    "china_data3 = gdf3[gdf3[\"COUNTRY\"] == \"China\"]\n",
    "# 输出符合条件的记录条数\n",
    "print(f\"符合条件的中国数据条数：{len(china_data3)}\")\n",
    "# 提取经纬度为列表\n",
    "latitudes3 = china_data3[\"LAT_RIV\"].tolist()\n",
    "longitudes3 = china_data3[\"LONG_RIV\"].tolist()\n",
    "print(len(latitudes3))\n",
    "print(len(longitudes3))\n",
    "# 组成经纬度元组\n",
    "coordinates3 = list(zip(longitudes3, latitudes3))\n",
    "print(len(coordinates3))\n",
    "print(coordinates3[:5])\n",
    "# 打印前几个经纬度元组\n",
    "for i in range(5):\n",
    "    print(f\"({coordinates3[i][0]}, {coordinates3[i][1]})\")\n",
    "# 加载中国地图\n",
    "china_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "china_map = china_map[china_map['name'] == 'China']\n",
    "# 绘制中国地图\n",
    "ax = china_map.plot(figsize=(10, 10), color='white', edgecolor='black')\n",
    "# 绘制经纬度点\n",
    "ax.scatter(longitudes3, latitudes3, color='green', s=10)\n",
    "# 隐藏坐标轴\n",
    "ax.axis('off')\n",
    "# 显示地图\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
